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mirror of https://github.com/paboyle/Grid.git synced 2025-06-17 15:27:06 +01:00

Merge branch 'develop' into feature/gpu-port

This commit is contained in:
Peter Boyle
2018-12-13 05:11:34 +00:00
647 changed files with 49155 additions and 11160 deletions

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/Algorithms.h
Copyright (C) 2015
Author: Azusa Yamaguchi <ayamaguc@staffmail.ed.ac.uk>
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_ALGORITHMS_H
#define GRID_ALGORITHMS_H
#include <Grid/algorithms/SparseMatrix.h>
#include <Grid/algorithms/LinearOperator.h>
#include <Grid/algorithms/Preconditioner.h>
#include <Grid/algorithms/approx/Zolotarev.h>
#include <Grid/algorithms/approx/Chebyshev.h>
#include <Grid/algorithms/approx/Remez.h>
#include <Grid/algorithms/approx/MultiShiftFunction.h>
#include <Grid/algorithms/approx/Forecast.h>
#include <Grid/algorithms/iterative/Deflation.h>
#include <Grid/algorithms/iterative/ConjugateGradient.h>
#include <Grid/algorithms/iterative/ConjugateResidual.h>
#include <Grid/algorithms/iterative/NormalEquations.h>
#include <Grid/algorithms/iterative/SchurRedBlack.h>
#include <Grid/algorithms/iterative/ConjugateGradientMultiShift.h>
#include <Grid/algorithms/iterative/ConjugateGradientMixedPrec.h>
#include <Grid/algorithms/iterative/BlockConjugateGradient.h>
#include <Grid/algorithms/iterative/ConjugateGradientReliableUpdate.h>
#include <Grid/algorithms/iterative/ImplicitlyRestartedLanczos.h>
#include <Grid/algorithms/CoarsenedMatrix.h>
#include <Grid/algorithms/FFT.h>
// EigCg
// Pcg
// Hdcg
// GCR
// etc..
#endif

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/CoarsenedMatrix.h
Copyright (C) 2015
Author: Azusa Yamaguchi <ayamaguc@staffmail.ed.ac.uk>
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
Author: Peter Boyle <peterboyle@Peters-MacBook-Pro-2.local>
Author: paboyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_ALGORITHM_COARSENED_MATRIX_H
#define GRID_ALGORITHM_COARSENED_MATRIX_H
NAMESPACE_BEGIN(Grid);
class Geometry {
// int dimension;
public:
int npoint;
std::vector<int> directions ;
std::vector<int> displacements;
Geometry(int _d) {
int base = (_d==5) ? 1:0;
// make coarse grid stencil for 4d , not 5d
if ( _d==5 ) _d=4;
npoint = 2*_d+1;
directions.resize(npoint);
displacements.resize(npoint);
for(int d=0;d<_d;d++){
directions[2*d ] = d+base;
directions[2*d+1] = d+base;
displacements[2*d ] = +1;
displacements[2*d+1] = -1;
}
directions [2*_d]=0;
displacements[2*_d]=0;
//// report back
std::cout<<GridLogMessage<<"directions :";
for(int d=0;d<npoint;d++) std::cout<< directions[d]<< " ";
std::cout <<std::endl;
std::cout<<GridLogMessage<<"displacements :";
for(int d=0;d<npoint;d++) std::cout<< displacements[d]<< " ";
std::cout<<std::endl;
}
/*
// Original cleaner code
Geometry(int _d) : dimension(_d), npoint(2*_d+1), directions(npoint), displacements(npoint) {
for(int d=0;d<dimension;d++){
directions[2*d ] = d;
directions[2*d+1] = d;
displacements[2*d ] = +1;
displacements[2*d+1] = -1;
}
directions [2*dimension]=0;
displacements[2*dimension]=0;
}
std::vector<int> GetDelta(int point) {
std::vector<int> delta(dimension,0);
delta[directions[point]] = displacements[point];
return delta;
};
*/
};
template<class Fobj,class CComplex,int nbasis>
class Aggregation {
public:
typedef iVector<CComplex,nbasis > siteVector;
typedef Lattice<siteVector> CoarseVector;
typedef Lattice<iMatrix<CComplex,nbasis > > CoarseMatrix;
typedef Lattice< CComplex > CoarseScalar; // used for inner products on fine field
typedef Lattice<Fobj > FineField;
GridBase *CoarseGrid;
GridBase *FineGrid;
std::vector<Lattice<Fobj> > subspace;
int checkerboard;
int Checkerboard(void){return checkerboard;}
Aggregation(GridBase *_CoarseGrid,GridBase *_FineGrid,int _checkerboard) :
CoarseGrid(_CoarseGrid),
FineGrid(_FineGrid),
subspace(nbasis,_FineGrid),
checkerboard(_checkerboard)
{
};
void Orthogonalise(void){
CoarseScalar InnerProd(CoarseGrid);
std::cout << GridLogMessage <<" Gramm-Schmidt pass 1"<<std::endl;
blockOrthogonalise(InnerProd,subspace);
std::cout << GridLogMessage <<" Gramm-Schmidt pass 2"<<std::endl;
blockOrthogonalise(InnerProd,subspace);
// std::cout << GridLogMessage <<" Gramm-Schmidt checking orthogonality"<<std::endl;
// CheckOrthogonal();
}
void CheckOrthogonal(void){
CoarseVector iProj(CoarseGrid);
CoarseVector eProj(CoarseGrid);
for(int i=0;i<nbasis;i++){
blockProject(iProj,subspace[i],subspace);
eProj=Zero();
thread_loop( (int ss=0;ss<CoarseGrid->oSites();ss++),{
eProj[ss](i)=CComplex(1.0);
});
eProj=eProj - iProj;
std::cout<<GridLogMessage<<"Orthog check error "<<i<<" " << norm2(eProj)<<std::endl;
}
std::cout<<GridLogMessage <<"CheckOrthog done"<<std::endl;
}
void ProjectToSubspace(CoarseVector &CoarseVec,const FineField &FineVec){
blockProject(CoarseVec,FineVec,subspace);
}
void PromoteFromSubspace(const CoarseVector &CoarseVec,FineField &FineVec){
FineVec.Checkerboard() = subspace[0].Checkerboard();
blockPromote(CoarseVec,FineVec,subspace);
}
void CreateSubspaceRandom(GridParallelRNG &RNG){
for(int i=0;i<nbasis;i++){
random(RNG,subspace[i]);
std::cout<<GridLogMessage<<" norm subspace["<<i<<"] "<<norm2(subspace[i])<<std::endl;
}
Orthogonalise();
}
/*
virtual void CreateSubspaceLanczos(GridParallelRNG &RNG,LinearOperatorBase<FineField> &hermop,int nn=nbasis)
{
// Run a Lanczos with sloppy convergence
const int Nstop = nn;
const int Nk = nn+20;
const int Np = nn+20;
const int Nm = Nk+Np;
const int MaxIt= 10000;
RealD resid = 1.0e-3;
Chebyshev<FineField> Cheb(0.5,64.0,21);
ImplicitlyRestartedLanczos<FineField> IRL(hermop,Cheb,Nstop,Nk,Nm,resid,MaxIt);
// IRL.lock = 1;
FineField noise(FineGrid); gaussian(RNG,noise);
FineField tmp(FineGrid);
std::vector<RealD> eval(Nm);
std::vector<FineField> evec(Nm,FineGrid);
int Nconv;
IRL.calc(eval,evec,
noise,
Nconv);
// pull back nn vectors
for(int b=0;b<nn;b++){
subspace[b] = evec[b];
std::cout << GridLogMessage <<"subspace["<<b<<"] = "<<norm2(subspace[b])<<std::endl;
hermop.Op(subspace[b],tmp);
std::cout<<GridLogMessage << "filtered["<<b<<"] <f|MdagM|f> "<<norm2(tmp)<<std::endl;
noise = tmp - sqrt(eval[b])*subspace[b] ;
std::cout<<GridLogMessage << " lambda_"<<b<<" = "<< eval[b] <<" ; [ M - Lambda ]_"<<b<<" vec_"<<b<<" = " <<norm2(noise)<<std::endl;
noise = tmp + eval[b]*subspace[b] ;
std::cout<<GridLogMessage << " lambda_"<<b<<" = "<< eval[b] <<" ; [ M - Lambda ]_"<<b<<" vec_"<<b<<" = " <<norm2(noise)<<std::endl;
}
Orthogonalise();
for(int b=0;b<nn;b++){
std::cout << GridLogMessage <<"subspace["<<b<<"] = "<<norm2(subspace[b])<<std::endl;
}
}
*/
virtual void CreateSubspace(GridParallelRNG &RNG,LinearOperatorBase<FineField> &hermop,int nn=nbasis) {
RealD scale;
ConjugateGradient<FineField> CG(1.0e-2,10000);
FineField noise(FineGrid);
FineField Mn(FineGrid);
for(int b=0;b<nn;b++){
gaussian(RNG,noise);
scale = std::pow(norm2(noise),-0.5);
noise=noise*scale;
hermop.Op(noise,Mn); std::cout<<GridLogMessage << "noise ["<<b<<"] <n|MdagM|n> "<<norm2(Mn)<<std::endl;
for(int i=0;i<1;i++){
CG(hermop,noise,subspace[b]);
noise = subspace[b];
scale = std::pow(norm2(noise),-0.5);
noise=noise*scale;
}
hermop.Op(noise,Mn); std::cout<<GridLogMessage << "filtered["<<b<<"] <f|MdagM|f> "<<norm2(Mn)<<std::endl;
subspace[b] = noise;
}
Orthogonalise();
}
};
// Fine Object == (per site) type of fine field
// nbasis == number of deflation vectors
template<class Fobj,class CComplex,int nbasis>
class CoarsenedMatrix : public SparseMatrixBase<Lattice<iVector<CComplex,nbasis > > > {
public:
typedef iVector<CComplex,nbasis > siteVector;
typedef Lattice<siteVector> CoarseVector;
typedef Lattice<iMatrix<CComplex,nbasis > > CoarseMatrix;
typedef Lattice< CComplex > CoarseScalar; // used for inner products on fine field
typedef Lattice<Fobj > FineField;
////////////////////
// Data members
////////////////////
Geometry geom;
GridBase * _grid;
CartesianStencil<siteVector,siteVector> Stencil;
std::vector<CoarseMatrix> A;
///////////////////////
// Interface
///////////////////////
GridBase * Grid(void) { return _grid; }; // this is all the linalg routines need to know
RealD M (const CoarseVector &in, CoarseVector &out){
conformable(_grid,in.Grid());
conformable(in.Grid(),out.Grid());
SimpleCompressor<siteVector> compressor;
Stencil.HaloExchange(in,compressor);
auto in_v = in.View();
auto out_v = in.View();
thread_loop( (int ss=0;ss<Grid()->oSites();ss++),{
siteVector res = Zero();
siteVector nbr;
int ptype;
StencilEntry *SE;
for(int point=0;point<geom.npoint;point++){
SE=Stencil.GetEntry(ptype,point,ss);
if(SE->_is_local&&SE->_permute) {
permute(nbr,in_v[SE->_offset],ptype);
} else if(SE->_is_local) {
nbr = in_v[SE->_offset];
} else {
nbr = Stencil.CommBuf()[SE->_offset];
}
auto A_point = A[point].View();
res = res + A_point[ss]*nbr;
}
vstream(out_v[ss],res);
});
return norm2(out);
};
RealD Mdag (const CoarseVector &in, CoarseVector &out){
return M(in,out);
};
// Defer support for further coarsening for now
void Mdiag (const CoarseVector &in, CoarseVector &out){};
void Mdir (const CoarseVector &in, CoarseVector &out,int dir, int disp){};
CoarsenedMatrix(GridCartesian &CoarseGrid) :
_grid(&CoarseGrid),
geom(CoarseGrid._ndimension),
Stencil(&CoarseGrid,geom.npoint,Even,geom.directions,geom.displacements),
A(geom.npoint,&CoarseGrid)
{
};
void CoarsenOperator(GridBase *FineGrid,LinearOperatorBase<Lattice<Fobj> > &linop,
Aggregation<Fobj,CComplex,nbasis> & Subspace){
FineField iblock(FineGrid); // contributions from within this block
FineField oblock(FineGrid); // contributions from outwith this block
FineField phi(FineGrid);
FineField tmp(FineGrid);
FineField zz(FineGrid); zz=Zero();
FineField Mphi(FineGrid);
Lattice<iScalar<vInteger> > coor(FineGrid);
CoarseVector iProj(Grid());
CoarseVector oProj(Grid());
CoarseScalar InnerProd(Grid());
// Orthogonalise the subblocks over the basis
blockOrthogonalise(InnerProd,Subspace.subspace);
// Compute the matrix elements of linop between this orthonormal
// set of vectors.
int self_stencil=-1;
for(int p=0;p<geom.npoint;p++){
A[p]=Zero();
if( geom.displacements[p]==0){
self_stencil=p;
}
}
assert(self_stencil!=-1);
for(int i=0;i<nbasis;i++){
phi=Subspace.subspace[i];
std::cout<<GridLogMessage<<"("<<i<<").."<<std::endl;
for(int p=0;p<geom.npoint;p++){
int dir = geom.directions[p];
int disp = geom.displacements[p];
Integer block=(FineGrid->_rdimensions[dir])/(Grid()->_rdimensions[dir]);
LatticeCoordinate(coor,dir);
if ( disp==0 ){
linop.OpDiag(phi,Mphi);
}
else {
linop.OpDir(phi,Mphi,dir,disp);
}
////////////////////////////////////////////////////////////////////////
// Pick out contributions coming from this cell and neighbour cell
////////////////////////////////////////////////////////////////////////
if ( disp==0 ) {
iblock = Mphi;
oblock = Zero();
} else if ( disp==1 ) {
oblock = where(mod(coor,block)==(block-1),Mphi,zz);
iblock = where(mod(coor,block)!=(block-1),Mphi,zz);
} else if ( disp==-1 ) {
oblock = where(mod(coor,block)==(Integer)0,Mphi,zz);
iblock = where(mod(coor,block)!=(Integer)0,Mphi,zz);
} else {
assert(0);
}
Subspace.ProjectToSubspace(iProj,iblock);
Subspace.ProjectToSubspace(oProj,oblock);
// blockProject(iProj,iblock,Subspace.subspace);
// blockProject(oProj,oblock,Subspace.subspace);
auto iProj_v = iProj.View() ;
auto oProj_v = oProj.View() ;
auto A_p = A[p].View();
auto A_self = A[self_stencil].View();
thread_loop( (int ss=0;ss<Grid()->oSites();ss++),{
for(int j=0;j<nbasis;j++){
if( disp!= 0 ) {
A_p[ss](j,i) = oProj_v[ss](j);
}
A_self[ss](j,i) = A_self[ss](j,i) + iProj_v[ss](j);
}
});
}
}
#if 0
///////////////////////////
// test code worth preserving in if block
///////////////////////////
std::cout<<GridLogMessage<< " Computed matrix elements "<< self_stencil <<std::endl;
for(int p=0;p<geom.npoint;p++){
std::cout<<GridLogMessage<< "A["<<p<<"]" << std::endl;
std::cout<<GridLogMessage<< A[p] << std::endl;
}
std::cout<<GridLogMessage<< " picking by block0 "<< self_stencil <<std::endl;
phi=Subspace.subspace[0];
std::vector<int> bc(FineGrid->_ndimension,0);
blockPick(Grid(),phi,tmp,bc); // Pick out a block
linop.Op(tmp,Mphi); // Apply big dop
blockProject(iProj,Mphi,Subspace.subspace); // project it and print it
std::cout<<GridLogMessage<< " Computed matrix elements from block zero only "<<std::endl;
std::cout<<GridLogMessage<< iProj <<std::endl;
std::cout<<GridLogMessage<<"Computed Coarse Operator"<<std::endl;
#endif
// ForceHermitian();
AssertHermitian();
// ForceDiagonal();
}
void ForceDiagonal(void) {
std::cout<<GridLogMessage<<"**************************************************"<<std::endl;
std::cout<<GridLogMessage<<"**** Forcing coarse operator to be diagonal ****"<<std::endl;
std::cout<<GridLogMessage<<"**************************************************"<<std::endl;
for(int p=0;p<8;p++){
A[p]=Zero();
}
GridParallelRNG RNG(Grid()); RNG.SeedFixedIntegers(std::vector<int>({55,72,19,17,34}));
Lattice<iScalar<CComplex> > val(Grid()); random(RNG,val);
Complex one(1.0);
iMatrix<CComplex,nbasis> ident; ident=one;
val = val*adj(val);
val = val + 1.0;
A[8] = val*ident;
// for(int s=0;s<Grid()->oSites();s++) {
// A[8][s]=val[s];
// }
}
void ForceHermitian(void) {
for(int d=0;d<4;d++){
int dd=d+1;
A[2*d] = adj(Cshift(A[2*d+1],dd,1));
}
// A[8] = 0.5*(A[8] + adj(A[8]));
}
void AssertHermitian(void) {
CoarseMatrix AA (Grid());
CoarseMatrix AAc (Grid());
CoarseMatrix Diff (Grid());
for(int d=0;d<4;d++){
int dd=d+1;
AAc = Cshift(A[2*d+1],dd,1);
AA = A[2*d];
Diff = AA - adj(AAc);
std::cout<<GridLogMessage<<"Norm diff dim "<<d<<" "<< norm2(Diff)<<std::endl;
std::cout<<GridLogMessage<<"Norm dim "<<d<<" "<< norm2(AA)<<std::endl;
}
Diff = A[8] - adj(A[8]);
std::cout<<GridLogMessage<<"Norm diff local "<< norm2(Diff)<<std::endl;
std::cout<<GridLogMessage<<"Norm local "<< norm2(A[8])<<std::endl;
}
};
NAMESPACE_END(Grid);
#endif

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/Cshift.h
Copyright (C) 2015
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef _GRID_FFT_H_
#define _GRID_FFT_H_
#ifdef HAVE_FFTW
#ifdef USE_MKL
#include <fftw/fftw3.h>
#else
#include <fftw3.h>
#endif
#endif
NAMESPACE_BEGIN(Grid);
template<class scalar> struct FFTW { };
#ifdef HAVE_FFTW
template<> struct FFTW<ComplexD> {
public:
typedef fftw_complex FFTW_scalar;
typedef fftw_plan FFTW_plan;
static FFTW_plan fftw_plan_many_dft(int rank, const int *n,int howmany,
FFTW_scalar *in, const int *inembed,
int istride, int idist,
FFTW_scalar *out, const int *onembed,
int ostride, int odist,
int sign, unsigned flags) {
return ::fftw_plan_many_dft(rank,n,howmany,in,inembed,istride,idist,out,onembed,ostride,odist,sign,flags);
}
static void fftw_flops(const FFTW_plan p,double *add, double *mul, double *fmas){
::fftw_flops(p,add,mul,fmas);
}
inline static void fftw_execute_dft(const FFTW_plan p,FFTW_scalar *in,FFTW_scalar *out) {
::fftw_execute_dft(p,in,out);
}
inline static void fftw_destroy_plan(const FFTW_plan p) {
::fftw_destroy_plan(p);
}
};
template<> struct FFTW<ComplexF> {
public:
typedef fftwf_complex FFTW_scalar;
typedef fftwf_plan FFTW_plan;
static FFTW_plan fftw_plan_many_dft(int rank, const int *n,int howmany,
FFTW_scalar *in, const int *inembed,
int istride, int idist,
FFTW_scalar *out, const int *onembed,
int ostride, int odist,
int sign, unsigned flags) {
return ::fftwf_plan_many_dft(rank,n,howmany,in,inembed,istride,idist,out,onembed,ostride,odist,sign,flags);
}
static void fftw_flops(const FFTW_plan p,double *add, double *mul, double *fmas){
::fftwf_flops(p,add,mul,fmas);
}
inline static void fftw_execute_dft(const FFTW_plan p,FFTW_scalar *in,FFTW_scalar *out) {
::fftwf_execute_dft(p,in,out);
}
inline static void fftw_destroy_plan(const FFTW_plan p) {
::fftwf_destroy_plan(p);
}
};
#endif
#ifndef FFTW_FORWARD
#define FFTW_FORWARD (-1)
#define FFTW_BACKWARD (+1)
#endif
class FFT {
private:
GridCartesian *vgrid;
GridCartesian *sgrid;
int Nd;
double flops;
double flops_call;
uint64_t usec;
Coordinate dimensions;
Coordinate processors;
Coordinate processor_coor;
public:
static const int forward=FFTW_FORWARD;
static const int backward=FFTW_BACKWARD;
double Flops(void) {return flops;}
double MFlops(void) {return flops/usec;}
double USec(void) {return (double)usec;}
FFT ( GridCartesian * grid ) :
vgrid(grid),
Nd(grid->_ndimension),
dimensions(grid->_fdimensions),
processors(grid->_processors),
processor_coor(grid->_processor_coor)
{
flops=0;
usec =0;
Coordinate layout(Nd,1);
sgrid = new GridCartesian(dimensions,layout,processors);
};
~FFT ( void) {
delete sgrid;
}
template<class vobj>
void FFT_dim_mask(Lattice<vobj> &result,const Lattice<vobj> &source,Coordinate mask,int sign){
conformable(result.Grid(),vgrid);
conformable(source.Grid(),vgrid);
Lattice<vobj> tmp(vgrid);
tmp = source;
for(int d=0;d<Nd;d++){
if( mask[d] ) {
FFT_dim(result,tmp,d,sign);
tmp=result;
}
}
}
template<class vobj>
void FFT_all_dim(Lattice<vobj> &result,const Lattice<vobj> &source,int sign){
Coordinate mask(Nd,1);
FFT_dim_mask(result,source,mask,sign);
}
template<class vobj>
void FFT_dim(Lattice<vobj> &result,const Lattice<vobj> &source,int dim, int sign){
#ifndef HAVE_FFTW
assert(0);
#else
conformable(result.Grid(),vgrid);
conformable(source.Grid(),vgrid);
int L = vgrid->_ldimensions[dim];
int G = vgrid->_fdimensions[dim];
Coordinate layout(Nd,1);
Coordinate pencil_gd(vgrid->_fdimensions);
pencil_gd[dim] = G*processors[dim];
// Pencil global vol LxLxGxLxL per node
GridCartesian pencil_g(pencil_gd,layout,processors);
// Construct pencils
typedef typename vobj::scalar_object sobj;
typedef typename sobj::scalar_type scalar;
Lattice<sobj> pgbuf(&pencil_g);
auto pgbuf_v = pgbuf.View();
typedef typename FFTW<scalar>::FFTW_scalar FFTW_scalar;
typedef typename FFTW<scalar>::FFTW_plan FFTW_plan;
int Ncomp = sizeof(sobj)/sizeof(scalar);
int Nlow = 1;
for(int d=0;d<dim;d++){
Nlow*=vgrid->_ldimensions[d];
}
int rank = 1; /* 1d transforms */
int n[] = {G}; /* 1d transforms of length G */
int howmany = Ncomp;
int odist,idist,istride,ostride;
idist = odist = 1; /* Distance between consecutive FT's */
istride = ostride = Ncomp*Nlow; /* distance between two elements in the same FT */
int *inembed = n, *onembed = n;
scalar div;
if ( sign == backward ) div = 1.0/G;
else if ( sign == forward ) div = 1.0;
else assert(0);
FFTW_plan p;
{
FFTW_scalar *in = (FFTW_scalar *)&pgbuf_v[0];
FFTW_scalar *out= (FFTW_scalar *)&pgbuf_v[0];
p = FFTW<scalar>::fftw_plan_many_dft(rank,n,howmany,
in,inembed,
istride,idist,
out,onembed,
ostride, odist,
sign,FFTW_ESTIMATE);
}
// Barrel shift and collect global pencil
Coordinate lcoor(Nd), gcoor(Nd);
result = source;
int pc = processor_coor[dim];
for(int p=0;p<processors[dim];p++) {
thread_loop( (int idx=0;idx<sgrid->lSites();idx++), {
Coordinate cbuf(Nd);
sobj s;
sgrid->LocalIndexToLocalCoor(idx,cbuf);
peekLocalSite(s,result,cbuf);
cbuf[dim]+=((pc+p) % processors[dim])*L;
// cbuf[dim]+=p*L;
pokeLocalSite(s,pgbuf,cbuf);
});
if (p != processors[dim] - 1) {
result = Cshift(result,dim,L);
}
}
// Loop over orthog coords
int NN=pencil_g.lSites();
GridStopWatch timer;
timer.Start();
thread_loop( (int idx=0;idx<NN;idx++), {
Coordinate cbuf(Nd);
pencil_g.LocalIndexToLocalCoor(idx, cbuf);
if ( cbuf[dim] == 0 ) { // restricts loop to plane at lcoor[dim]==0
FFTW_scalar *in = (FFTW_scalar *)&pgbuf_v[idx];
FFTW_scalar *out= (FFTW_scalar *)&pgbuf_v[idx];
FFTW<scalar>::fftw_execute_dft(p,in,out);
}
});
timer.Stop();
// performance counting
double add,mul,fma;
FFTW<scalar>::fftw_flops(p,&add,&mul,&fma);
flops_call = add+mul+2.0*fma;
usec += timer.useconds();
flops+= flops_call*NN;
// writing out result
thread_loop( (int idx=0;idx<sgrid->lSites();idx++), {
Coordinate clbuf(Nd), cgbuf(Nd);
sobj s;
sgrid->LocalIndexToLocalCoor(idx,clbuf);
cgbuf = clbuf;
cgbuf[dim] = clbuf[dim]+L*pc;
peekLocalSite(s,pgbuf,cgbuf);
pokeLocalSite(s,result,clbuf);
});
result = result*div;
// destroying plan
FFTW<scalar>::fftw_destroy_plan(p);
#endif
}
};
NAMESPACE_END(Grid);
#endif

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@ -0,0 +1,498 @@
/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/LinearOperator.h
Copyright (C) 2015
Author: Azusa Yamaguchi <ayamaguc@staffmail.ed.ac.uk>
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_ALGORITHM_LINEAR_OP_H
#define GRID_ALGORITHM_LINEAR_OP_H
NAMESPACE_BEGIN(Grid);
/////////////////////////////////////////////////////////////////////////////////////////////
// LinearOperators Take a something and return a something.
/////////////////////////////////////////////////////////////////////////////////////////////
//
// Hopefully linearity is satisfied and the AdjOp is indeed the Hermitian Conjugateugate (transpose if real):
//SBase
// i) F(a x + b y) = aF(x) + b F(y).
// ii) <x|Op|y> = <y|AdjOp|x>^\ast
//
// Would be fun to have a test linearity & Herm Conj function!
/////////////////////////////////////////////////////////////////////////////////////////////
template<class Field> class LinearOperatorBase {
public:
// Support for coarsening to a multigrid
virtual void OpDiag (const Field &in, Field &out) = 0; // Abstract base
virtual void OpDir (const Field &in, Field &out,int dir,int disp) = 0; // Abstract base
virtual void Op (const Field &in, Field &out) = 0; // Abstract base
virtual void AdjOp (const Field &in, Field &out) = 0; // Abstract base
virtual void HermOpAndNorm(const Field &in, Field &out,RealD &n1,RealD &n2)=0;
virtual void HermOp(const Field &in, Field &out)=0;
};
/////////////////////////////////////////////////////////////////////////////////////////////
// By sharing the class for Sparse Matrix across multiple operator wrappers, we can share code
// between RB and non-RB variants. Sparse matrix is like the fermion action def, and then
// the wrappers implement the specialisation of "Op" and "AdjOp" to the cases minimising
// replication of code.
//
// I'm not entirely happy with implementation; to share the Schur code between herm and non-herm
// while still having a "OpAndNorm" in the abstract base I had to implement it in both cases
// with an assert trap in the non-herm. This isn't right; there must be a better C++ way to
// do it, but I fear it required multiple inheritance and mixed in abstract base classes
/////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////
// Construct herm op from non-herm matrix
////////////////////////////////////////////////////////////////////
template<class Matrix,class Field>
class MdagMLinearOperator : public LinearOperatorBase<Field> {
Matrix &_Mat;
public:
MdagMLinearOperator(Matrix &Mat): _Mat(Mat){};
// Support for coarsening to a multigrid
void OpDiag (const Field &in, Field &out) {
_Mat.Mdiag(in,out);
}
void OpDir (const Field &in, Field &out,int dir,int disp) {
_Mat.Mdir(in,out,dir,disp);
}
void Op (const Field &in, Field &out){
_Mat.M(in,out);
}
void AdjOp (const Field &in, Field &out){
_Mat.Mdag(in,out);
}
void HermOpAndNorm(const Field &in, Field &out,RealD &n1,RealD &n2){
_Mat.MdagM(in,out,n1,n2);
}
void HermOp(const Field &in, Field &out){
RealD n1,n2;
HermOpAndNorm(in,out,n1,n2);
}
};
////////////////////////////////////////////////////////////////////
// Construct herm op and shift it for mgrid smoother
////////////////////////////////////////////////////////////////////
template<class Matrix,class Field>
class ShiftedMdagMLinearOperator : public LinearOperatorBase<Field> {
Matrix &_Mat;
RealD _shift;
public:
ShiftedMdagMLinearOperator(Matrix &Mat,RealD shift): _Mat(Mat), _shift(shift){};
// Support for coarsening to a multigrid
void OpDiag (const Field &in, Field &out) {
_Mat.Mdiag(in,out);
assert(0);
}
void OpDir (const Field &in, Field &out,int dir,int disp) {
_Mat.Mdir(in,out,dir,disp);
assert(0);
}
void Op (const Field &in, Field &out){
_Mat.M(in,out);
assert(0);
}
void AdjOp (const Field &in, Field &out){
_Mat.Mdag(in,out);
assert(0);
}
void HermOpAndNorm(const Field &in, Field &out,RealD &n1,RealD &n2){
_Mat.MdagM(in,out,n1,n2);
out = out + _shift*in;
ComplexD dot;
dot= innerProduct(in,out);
n1=real(dot);
n2=norm2(out);
}
void HermOp(const Field &in, Field &out){
RealD n1,n2;
HermOpAndNorm(in,out,n1,n2);
}
};
////////////////////////////////////////////////////////////////////
// Wrap an already herm matrix
////////////////////////////////////////////////////////////////////
template<class Matrix,class Field>
class HermitianLinearOperator : public LinearOperatorBase<Field> {
Matrix &_Mat;
public:
HermitianLinearOperator(Matrix &Mat): _Mat(Mat){};
// Support for coarsening to a multigrid
void OpDiag (const Field &in, Field &out) {
_Mat.Mdiag(in,out);
}
void OpDir (const Field &in, Field &out,int dir,int disp) {
_Mat.Mdir(in,out,dir,disp);
}
void Op (const Field &in, Field &out){
_Mat.M(in,out);
}
void AdjOp (const Field &in, Field &out){
_Mat.M(in,out);
}
void HermOpAndNorm(const Field &in, Field &out,RealD &n1,RealD &n2){
_Mat.M(in,out);
ComplexD dot= innerProduct(in,out); n1=real(dot);
n2=norm2(out);
}
void HermOp(const Field &in, Field &out){
_Mat.M(in,out);
}
};
//////////////////////////////////////////////////////////
// Even Odd Schur decomp operators; there are several
// ways to introduce the even odd checkerboarding
//////////////////////////////////////////////////////////
template<class Field>
class SchurOperatorBase : public LinearOperatorBase<Field> {
public:
virtual RealD Mpc (const Field &in, Field &out) =0;
virtual RealD MpcDag (const Field &in, Field &out) =0;
virtual void MpcDagMpc(const Field &in, Field &out,RealD &ni,RealD &no)
{
Field tmp(in.Grid());
tmp.Checkerboard() = in.Checkerboard();
ni=Mpc(in,tmp);
no=MpcDag(tmp,out);
}
virtual void HermOpAndNorm(const Field &in, Field &out,RealD &n1,RealD &n2){
out.Checkerboard() = in.Checkerboard();
MpcDagMpc(in,out,n1,n2);
}
virtual void HermOp(const Field &in, Field &out){
RealD n1,n2;
HermOpAndNorm(in,out,n1,n2);
}
void Op (const Field &in, Field &out){
Mpc(in,out);
}
void AdjOp (const Field &in, Field &out){
MpcDag(in,out);
}
// Support for coarsening to a multigrid
void OpDiag (const Field &in, Field &out) {
assert(0); // must coarsen the unpreconditioned system
}
void OpDir (const Field &in, Field &out,int dir,int disp) {
assert(0);
}
};
template<class Matrix,class Field>
class SchurDiagMooeeOperator : public SchurOperatorBase<Field> {
protected:
Matrix &_Mat;
public:
SchurDiagMooeeOperator (Matrix &Mat): _Mat(Mat){};
virtual RealD Mpc (const Field &in, Field &out) {
Field tmp(in.Grid());
// std::cout <<"grid pointers: in.Grid()="<< in.Grid() << " out.Grid()=" << out.Grid() << " _Mat.Grid=" << _Mat.Grid() << " _Mat.RedBlackGrid=" << _Mat.RedBlackGrid() << std::endl;
tmp.Checkerboard() = !in.Checkerboard();
_Mat.Meooe(in,tmp);
_Mat.MooeeInv(tmp,out);
_Mat.Meooe(out,tmp);
//std::cout << "cb in " << in.Checkerboard() << " cb out " << out.Checkerboard() << std::endl;
_Mat.Mooee(in,out);
return axpy_norm(out,-1.0,tmp,out);
}
virtual RealD MpcDag (const Field &in, Field &out){
Field tmp(in.Grid());
_Mat.MeooeDag(in,tmp);
_Mat.MooeeInvDag(tmp,out);
_Mat.MeooeDag(out,tmp);
_Mat.MooeeDag(in,out);
return axpy_norm(out,-1.0,tmp,out);
}
};
template<class Matrix,class Field>
class SchurDiagOneOperator : public SchurOperatorBase<Field> {
protected:
Matrix &_Mat;
public:
SchurDiagOneOperator (Matrix &Mat): _Mat(Mat){};
virtual RealD Mpc (const Field &in, Field &out) {
Field tmp(in.Grid());
_Mat.Meooe(in,out);
_Mat.MooeeInv(out,tmp);
_Mat.Meooe(tmp,out);
_Mat.MooeeInv(out,tmp);
return axpy_norm(out,-1.0,tmp,in);
}
virtual RealD MpcDag (const Field &in, Field &out){
Field tmp(in.Grid());
_Mat.MooeeInvDag(in,out);
_Mat.MeooeDag(out,tmp);
_Mat.MooeeInvDag(tmp,out);
_Mat.MeooeDag(out,tmp);
return axpy_norm(out,-1.0,tmp,in);
}
};
template<class Matrix,class Field>
class SchurDiagTwoOperator : public SchurOperatorBase<Field> {
protected:
Matrix &_Mat;
public:
SchurDiagTwoOperator (Matrix &Mat): _Mat(Mat){};
virtual RealD Mpc (const Field &in, Field &out) {
Field tmp(in.Grid());
_Mat.MooeeInv(in,out);
_Mat.Meooe(out,tmp);
_Mat.MooeeInv(tmp,out);
_Mat.Meooe(out,tmp);
return axpy_norm(out,-1.0,tmp,in);
}
virtual RealD MpcDag (const Field &in, Field &out){
Field tmp(in.Grid());
_Mat.MeooeDag(in,out);
_Mat.MooeeInvDag(out,tmp);
_Mat.MeooeDag(tmp,out);
_Mat.MooeeInvDag(out,tmp);
return axpy_norm(out,-1.0,tmp,in);
}
};
///////////////////////////////////////////////////////////////////////////////////////////////////
// Left handed Moo^-1 ; (Moo - Moe Mee^-1 Meo) psi = eta --> ( 1 - Moo^-1 Moe Mee^-1 Meo ) psi = Moo^-1 eta
// Right handed Moo^-1 ; (Moo - Moe Mee^-1 Meo) Moo^-1 Moo psi = eta --> ( 1 - Moe Mee^-1 Meo ) Moo^-1 phi=eta ; psi = Moo^-1 phi
///////////////////////////////////////////////////////////////////////////////////////////////////
template<class Matrix,class Field> using SchurDiagOneRH = SchurDiagTwoOperator<Matrix,Field> ;
template<class Matrix,class Field> using SchurDiagOneLH = SchurDiagOneOperator<Matrix,Field> ;
///////////////////////////////////////////////////////////////////////////////////////////////////
// Staggered use
///////////////////////////////////////////////////////////////////////////////////////////////////
template<class Matrix,class Field>
class SchurStaggeredOperator : public SchurOperatorBase<Field> {
protected:
Matrix &_Mat;
Field tmp;
RealD mass;
double tMpc;
double tIP;
double tMeo;
double taxpby_norm;
uint64_t ncall;
public:
void Report(void)
{
std::cout << GridLogMessage << " HermOpAndNorm.Mpc "<< tMpc/ncall<<" usec "<<std::endl;
std::cout << GridLogMessage << " HermOpAndNorm.IP "<< tIP /ncall<<" usec "<<std::endl;
std::cout << GridLogMessage << " Mpc.MeoMoe "<< tMeo/ncall<<" usec "<<std::endl;
std::cout << GridLogMessage << " Mpc.axpby_norm "<< taxpby_norm/ncall<<" usec "<<std::endl;
}
SchurStaggeredOperator (Matrix &Mat): _Mat(Mat), tmp(_Mat.RedBlackGrid())
{
assert( _Mat.isTrivialEE() );
mass = _Mat.Mass();
tMpc=0;
tIP =0;
tMeo=0;
taxpby_norm=0;
ncall=0;
}
virtual void HermOpAndNorm(const Field &in, Field &out,RealD &n1,RealD &n2){
ncall++;
tMpc-=usecond();
n2 = Mpc(in,out);
tMpc+=usecond();
tIP-=usecond();
ComplexD dot= innerProduct(in,out);
tIP+=usecond();
n1 = real(dot);
}
virtual void HermOp(const Field &in, Field &out){
ncall++;
tMpc-=usecond();
_Mat.Meooe(in,out);
_Mat.Meooe(out,tmp);
tMpc+=usecond();
taxpby_norm-=usecond();
axpby(out,-1.0,mass*mass,tmp,in);
taxpby_norm+=usecond();
}
virtual RealD Mpc (const Field &in, Field &out)
{
Field tmp(in.Grid());
Field tmp2(in.Grid());
// std::cout << GridLogIterative << " HermOp.Mpc "<<std::endl;
_Mat.Mooee(in,out);
_Mat.Mooee(out,tmp);
// std::cout << GridLogIterative << " HermOp.MooeeMooee "<<std::endl;
tMeo-=usecond();
_Mat.Meooe(in,out);
_Mat.Meooe(out,tmp);
tMeo+=usecond();
taxpby_norm-=usecond();
RealD nn=axpby_norm(out,-1.0,mass*mass,tmp,in);
taxpby_norm+=usecond();
return nn;
}
virtual RealD MpcDag (const Field &in, Field &out){
return Mpc(in,out);
}
virtual void MpcDagMpc(const Field &in, Field &out,RealD &ni,RealD &no) {
assert(0);// Never need with staggered
}
};
template<class Matrix,class Field> using SchurStagOperator = SchurStaggeredOperator<Matrix,Field>;
/////////////////////////////////////////////////////////////
// Base classes for functions of operators
/////////////////////////////////////////////////////////////
template<class Field> class OperatorFunction {
public:
virtual void operator() (LinearOperatorBase<Field> &Linop, const Field &in, Field &out) = 0;
virtual void operator() (LinearOperatorBase<Field> &Linop, const std::vector<Field> &in,std::vector<Field> &out) {
assert(in.size()==out.size());
for(int k=0;k<in.size();k++){
(*this)(Linop,in[k],out[k]);
}
};
};
template<class Field> class LinearFunction {
public:
virtual void operator() (const Field &in, Field &out) = 0;
};
template<class Field> class IdentityLinearFunction : public LinearFunction<Field> {
public:
void operator() (const Field &in, Field &out){
out = in;
};
};
/////////////////////////////////////////////////////////////
// Base classes for Multishift solvers for operators
/////////////////////////////////////////////////////////////
template<class Field> class OperatorMultiFunction {
public:
virtual void operator() (LinearOperatorBase<Field> &Linop, const Field &in, std::vector<Field> &out) = 0;
};
// FIXME : To think about
// Chroma functionality list defining LinearOperator
/*
virtual void operator() (T& chi, const T& psi, enum PlusMinus isign) const = 0;
virtual void operator() (T& chi, const T& psi, enum PlusMinus isign, Real epsilon) const
virtual const Subset& subset() const = 0;
virtual unsigned long nFlops() const { return 0; }
virtual void deriv(P& ds_u, const T& chi, const T& psi, enum PlusMinus isign) const
class UnprecLinearOperator : public DiffLinearOperator<T,P,Q>
const Subset& subset() const {return all;}
};
*/
////////////////////////////////////////////////////////////////////////////////////////////
// Hermitian operator Linear function and operator function
////////////////////////////////////////////////////////////////////////////////////////////
template<class Field>
class HermOpOperatorFunction : public OperatorFunction<Field> {
void operator() (LinearOperatorBase<Field> &Linop, const Field &in, Field &out) {
Linop.HermOp(in,out);
};
};
template<typename Field>
class PlainHermOp : public LinearFunction<Field> {
public:
LinearOperatorBase<Field> &_Linop;
PlainHermOp(LinearOperatorBase<Field>& linop) : _Linop(linop)
{}
void operator()(const Field& in, Field& out) {
_Linop.HermOp(in,out);
}
};
template<typename Field>
class FunctionHermOp : public LinearFunction<Field> {
public:
OperatorFunction<Field> & _poly;
LinearOperatorBase<Field> &_Linop;
FunctionHermOp(OperatorFunction<Field> & poly,LinearOperatorBase<Field>& linop)
: _poly(poly), _Linop(linop) {};
void operator()(const Field& in, Field& out) {
_poly(_Linop,in,out);
}
};
template<class Field>
class Polynomial : public OperatorFunction<Field> {
private:
std::vector<RealD> Coeffs;
public:
Polynomial(std::vector<RealD> &_Coeffs) : Coeffs(_Coeffs) { };
// Implement the required interface
void operator() (LinearOperatorBase<Field> &Linop, const Field &in, Field &out) {
Field AtoN(in.Grid());
Field Mtmp(in.Grid());
AtoN = in;
out = AtoN*Coeffs[0];
for(int n=1;n<Coeffs.size();n++){
Mtmp = AtoN;
Linop.HermOp(Mtmp,AtoN);
out=out+AtoN*Coeffs[n];
}
};
};
NAMESPACE_END(Grid);
#endif

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/Preconditioner.h
Copyright (C) 2015
Author: Azusa Yamaguchi <ayamaguc@staffmail.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_PRECONDITIONER_H
#define GRID_PRECONDITIONER_H
NAMESPACE_BEGIN(Grid);
template<class Field> class Preconditioner : public LinearFunction<Field> {
virtual void operator()(const Field &src, Field & psi)=0;
};
template<class Field> class TrivialPrecon : public Preconditioner<Field> {
public:
void operator()(const Field &src, Field & psi){
psi = src;
}
TrivialPrecon(void){};
};
NAMESPACE_END(Grid);
#endif

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@ -0,0 +1,79 @@
/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/SparseMatrix.h
Copyright (C) 2015
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_ALGORITHM_SPARSE_MATRIX_H
#define GRID_ALGORITHM_SPARSE_MATRIX_H
NAMESPACE_BEGIN(Grid);
/////////////////////////////////////////////////////////////////////////////////////////////
// Interface defining what I expect of a general sparse matrix, such as a Fermion action
/////////////////////////////////////////////////////////////////////////////////////////////
template<class Field> class SparseMatrixBase {
public:
virtual GridBase *Grid(void) =0;
// Full checkerboar operations
virtual RealD M (const Field &in, Field &out)=0;
virtual RealD Mdag (const Field &in, Field &out)=0;
virtual void MdagM(const Field &in, Field &out,RealD &ni,RealD &no) {
Field tmp (in.Grid());
ni=M(in,tmp);
no=Mdag(tmp,out);
}
virtual void Mdiag (const Field &in, Field &out)=0;
virtual void Mdir (const Field &in, Field &out,int dir, int disp)=0;
};
/////////////////////////////////////////////////////////////////////////////////////////////
// Interface augmented by a red black sparse matrix, such as a Fermion action
/////////////////////////////////////////////////////////////////////////////////////////////
template<class Field> class CheckerBoardedSparseMatrixBase : public SparseMatrixBase<Field> {
public:
virtual GridBase *RedBlackGrid(void)=0;
//////////////////////////////////////////////////////////////////////
// Query the even even properties to make algorithmic decisions
//////////////////////////////////////////////////////////////////////
virtual RealD Mass(void) { return 0.0; };
virtual int ConstEE(void) { return 0; }; // Disable assumptions unless overridden
virtual int isTrivialEE(void) { return 0; }; // by a derived class that knows better
// half checkerboard operaions
virtual void Meooe (const Field &in, Field &out)=0;
virtual void Mooee (const Field &in, Field &out)=0;
virtual void MooeeInv (const Field &in, Field &out)=0;
virtual void MeooeDag (const Field &in, Field &out)=0;
virtual void MooeeDag (const Field &in, Field &out)=0;
virtual void MooeeInvDag (const Field &in, Field &out)=0;
};
NAMESPACE_END(Grid);
#endif

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@ -0,0 +1,377 @@
/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/approx/Chebyshev.h
Copyright (C) 2015
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
Author: paboyle <paboyle@ph.ed.ac.uk>
Author: Christoph Lehner <clehner@bnl.gov>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_CHEBYSHEV_H
#define GRID_CHEBYSHEV_H
#include <Grid/algorithms/LinearOperator.h>
NAMESPACE_BEGIN(Grid);
struct ChebyParams : Serializable {
GRID_SERIALIZABLE_CLASS_MEMBERS(ChebyParams,
RealD, alpha,
RealD, beta,
int, Npoly);
};
////////////////////////////////////////////////////////////////////////////////////////////
// Generic Chebyshev approximations
////////////////////////////////////////////////////////////////////////////////////////////
template<class Field>
class Chebyshev : public OperatorFunction<Field> {
private:
std::vector<RealD> Coeffs;
int order;
RealD hi;
RealD lo;
public:
void csv(std::ostream &out){
RealD diff = hi-lo;
RealD delta = diff*1.0e-9;
for (RealD x=lo; x<hi; x+=delta) {
delta*=1.1;
RealD f = approx(x);
out<< x<<" "<<f<<std::endl;
}
return;
}
// Convenience for plotting the approximation
void PlotApprox(std::ostream &out) {
out<<"Polynomial approx ["<<lo<<","<<hi<<"]"<<std::endl;
for(RealD x=lo;x<hi;x+=(hi-lo)/50.0){
out <<x<<"\t"<<approx(x)<<std::endl;
}
};
Chebyshev(){};
Chebyshev(ChebyParams p){ Init(p.alpha,p.beta,p.Npoly);};
Chebyshev(RealD _lo,RealD _hi,int _order, RealD (* func)(RealD) ) {Init(_lo,_hi,_order,func);};
Chebyshev(RealD _lo,RealD _hi,int _order) {Init(_lo,_hi,_order);};
////////////////////////////////////////////////////////////////////////////////////////////////////
// c.f. numerical recipes "chebft"/"chebev". This is sec 5.8 "Chebyshev approximation".
////////////////////////////////////////////////////////////////////////////////////////////////////
// CJ: the one we need for Lanczos
void Init(RealD _lo,RealD _hi,int _order)
{
lo=_lo;
hi=_hi;
order=_order;
if(order < 2) exit(-1);
Coeffs.resize(order);
Coeffs.assign(0.,order);
Coeffs[order-1] = 1.;
};
void Init(RealD _lo,RealD _hi,int _order, RealD (* func)(RealD))
{
lo=_lo;
hi=_hi;
order=_order;
if(order < 2) exit(-1);
Coeffs.resize(order);
for(int j=0;j<order;j++){
RealD s=0;
for(int k=0;k<order;k++){
RealD y=std::cos(M_PI*(k+0.5)/order);
RealD x=0.5*(y*(hi-lo)+(hi+lo));
RealD f=func(x);
s=s+f*std::cos( j*M_PI*(k+0.5)/order );
}
Coeffs[j] = s * 2.0/order;
}
};
void JacksonSmooth(void){
RealD M=order;
RealD alpha = M_PI/(M+2);
RealD lmax = std::cos(alpha);
RealD sumUsq =0;
std::vector<RealD> U(M);
std::vector<RealD> a(M);
std::vector<RealD> g(M);
for(int n=0;n<=M;n++){
U[n] = std::sin((n+1)*std::acos(lmax))/std::sin(std::acos(lmax));
sumUsq += U[n]*U[n];
}
sumUsq = std::sqrt(sumUsq);
for(int i=1;i<=M;i++){
a[i] = U[i]/sumUsq;
}
g[0] = 1.0;
for(int m=1;m<=M;m++){
g[m] = 0;
for(int i=0;i<=M-m;i++){
g[m]+= a[i]*a[m+i];
}
}
for(int m=1;m<=M;m++){
Coeffs[m]*=g[m];
}
}
RealD approx(RealD x) // Convenience for plotting the approximation
{
RealD Tn;
RealD Tnm;
RealD Tnp;
RealD y=( x-0.5*(hi+lo))/(0.5*(hi-lo));
RealD T0=1;
RealD T1=y;
RealD sum;
sum = 0.5*Coeffs[0]*T0;
sum+= Coeffs[1]*T1;
Tn =T1;
Tnm=T0;
for(int i=2;i<order;i++){
Tnp=2*y*Tn-Tnm;
Tnm=Tn;
Tn =Tnp;
sum+= Tn*Coeffs[i];
}
return sum;
};
RealD approxD(RealD x)
{
RealD Un;
RealD Unm;
RealD Unp;
RealD y=( x-0.5*(hi+lo))/(0.5*(hi-lo));
RealD U0=1;
RealD U1=2*y;
RealD sum;
sum = Coeffs[1]*U0;
sum+= Coeffs[2]*U1*2.0;
Un =U1;
Unm=U0;
for(int i=2;i<order-1;i++){
Unp=2*y*Un-Unm;
Unm=Un;
Un =Unp;
sum+= Un*Coeffs[i+1]*(i+1.0);
}
return sum/(0.5*(hi-lo));
};
RealD approxInv(RealD z, RealD x0, int maxiter, RealD resid) {
RealD x = x0;
RealD eps;
int i;
for (i=0;i<maxiter;i++) {
eps = approx(x) - z;
if (fabs(eps / z) < resid)
return x;
x = x - eps / approxD(x);
}
return std::numeric_limits<double>::quiet_NaN();
}
// Implement the required interface
void operator() (LinearOperatorBase<Field> &Linop, const Field &in, Field &out) {
GridBase *grid=in.Grid();
// std::cout << "Chevyshef(): in.Grid()="<<in.Grid()<<std::endl;
//std::cout <<" Linop.Grid()="<<Linop.Grid()<<"Linop.RedBlackGrid()="<<Linop.RedBlackGrid()<<std::endl;
int vol=grid->gSites();
Field T0(grid); T0 = in;
Field T1(grid);
Field T2(grid);
Field y(grid);
Field *Tnm = &T0;
Field *Tn = &T1;
Field *Tnp = &T2;
// Tn=T1 = (xscale M + mscale)in
RealD xscale = 2.0/(hi-lo);
RealD mscale = -(hi+lo)/(hi-lo);
Linop.HermOp(T0,y);
T1=y*xscale+in*mscale;
// sum = .5 c[0] T0 + c[1] T1
out = (0.5*Coeffs[0])*T0 + Coeffs[1]*T1;
for(int n=2;n<order;n++){
Linop.HermOp(*Tn,y);
y=xscale*y+mscale*(*Tn);
*Tnp=2.0*y-(*Tnm);
out=out+Coeffs[n]* (*Tnp);
// Cycle pointers to avoid copies
Field *swizzle = Tnm;
Tnm =Tn;
Tn =Tnp;
Tnp =swizzle;
}
}
};
template<class Field>
class ChebyshevLanczos : public Chebyshev<Field> {
private:
std::vector<RealD> Coeffs;
int order;
RealD alpha;
RealD beta;
RealD mu;
public:
ChebyshevLanczos(RealD _alpha,RealD _beta,RealD _mu,int _order) :
alpha(_alpha),
beta(_beta),
mu(_mu)
{
order=_order;
Coeffs.resize(order);
for(int i=0;i<_order;i++){
Coeffs[i] = 0.0;
}
Coeffs[order-1]=1.0;
};
void csv(std::ostream &out){
for (RealD x=-1.2*alpha; x<1.2*alpha; x+=(2.0*alpha)/10000) {
RealD f = approx(x);
out<< x<<" "<<f<<std::endl;
}
return;
}
RealD approx(RealD xx) // Convenience for plotting the approximation
{
RealD Tn;
RealD Tnm;
RealD Tnp;
Real aa = alpha * alpha;
Real bb = beta * beta;
RealD x = ( 2.0 * (xx-mu)*(xx-mu) - (aa+bb) ) / (aa-bb);
RealD y= x;
RealD T0=1;
RealD T1=y;
RealD sum;
sum = 0.5*Coeffs[0]*T0;
sum+= Coeffs[1]*T1;
Tn =T1;
Tnm=T0;
for(int i=2;i<order;i++){
Tnp=2*y*Tn-Tnm;
Tnm=Tn;
Tn =Tnp;
sum+= Tn*Coeffs[i];
}
return sum;
};
// shift_Multiply in Rudy's code
void AminusMuSq(LinearOperatorBase<Field> &Linop, const Field &in, Field &out)
{
GridBase *grid=in.Grid();
Field tmp(grid);
RealD aa= alpha*alpha;
RealD bb= beta * beta;
Linop.HermOp(in,out);
out = out - mu*in;
Linop.HermOp(out,tmp);
tmp = tmp - mu * out;
out = (2.0/ (aa-bb) ) * tmp - ((aa+bb)/(aa-bb))*in;
};
// Implement the required interface
void operator() (LinearOperatorBase<Field> &Linop, const Field &in, Field &out) {
GridBase *grid=in.Grid();
int vol=grid->gSites();
Field T0(grid); T0 = in;
Field T1(grid);
Field T2(grid);
Field y(grid);
Field *Tnm = &T0;
Field *Tn = &T1;
Field *Tnp = &T2;
// Tn=T1 = (xscale M )*in
AminusMuSq(Linop,T0,T1);
// sum = .5 c[0] T0 + c[1] T1
out = (0.5*Coeffs[0])*T0 + Coeffs[1]*T1;
for(int n=2;n<order;n++){
AminusMuSq(Linop,*Tn,y);
*Tnp=2.0*y-(*Tnm);
out=out+Coeffs[n]* (*Tnp);
// Cycle pointers to avoid copies
Field *swizzle = Tnm;
Tnm =Tn;
Tn =Tnp;
Tnp =swizzle;
}
}
};
NAMESPACE_END(Grid);
#endif

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/approx/Forecast.h
Copyright (C) 2015
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
Author: paboyle <paboyle@ph.ed.ac.uk>
Author: David Murphy <dmurphy@phys.columbia.edu>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef INCLUDED_FORECAST_H
#define INCLUDED_FORECAST_H
NAMESPACE_BEGIN(Grid);
// Abstract base class.
// Takes a matrix (Mat), a source (phi), and a vector of Fields (chi)
// and returns a forecasted solution to the system D*psi = phi (psi).
template<class Matrix, class Field>
class Forecast
{
public:
virtual Field operator()(Matrix &Mat, const Field& phi, const std::vector<Field>& chi) = 0;
};
// Implementation of Brower et al.'s chronological inverter (arXiv:hep-lat/9509012),
// used to forecast solutions across poles of the EOFA heatbath.
//
// Modified from CPS (cps_pp/src/util/dirac_op/d_op_base/comsrc/minresext.C)
template<class Matrix, class Field>
class ChronoForecast : public Forecast<Matrix,Field>
{
public:
Field operator()(Matrix &Mat, const Field& phi, const std::vector<Field>& prev_solns)
{
int degree = prev_solns.size();
Field chi(phi); // forecasted solution
// Trivial cases
if(degree == 0){ chi = Zero(); return chi; }
else if(degree == 1){ return prev_solns[0]; }
// RealD dot;
ComplexD xp;
Field r(phi); // residual
Field Mv(phi);
std::vector<Field> v(prev_solns); // orthonormalized previous solutions
std::vector<Field> MdagMv(degree,phi);
// Array to hold the matrix elements
std::vector<std::vector<ComplexD>> G(degree, std::vector<ComplexD>(degree));
// Solution and source vectors
std::vector<ComplexD> a(degree);
std::vector<ComplexD> b(degree);
// Orthonormalize the vector basis
for(int i=0; i<degree; i++){
v[i] *= 1.0/std::sqrt(norm2(v[i]));
for(int j=i+1; j<degree; j++){ v[j] -= innerProduct(v[i],v[j]) * v[i]; }
}
// Perform sparse matrix multiplication and construct rhs
for(int i=0; i<degree; i++){
b[i] = innerProduct(v[i],phi);
Mat.M(v[i],Mv);
Mat.Mdag(Mv,MdagMv[i]);
G[i][i] = innerProduct(v[i],MdagMv[i]);
}
// Construct the matrix
for(int j=0; j<degree; j++){
for(int k=j+1; k<degree; k++){
G[j][k] = innerProduct(v[j],MdagMv[k]);
G[k][j] = conjugate(G[j][k]);
}}
// Gauss-Jordan elimination with partial pivoting
for(int i=0; i<degree; i++){
// Perform partial pivoting
int k = i;
for(int j=i+1; j<degree; j++){ if(abs(G[j][j]) > abs(G[k][k])){ k = j; } }
if(k != i){
xp = b[k];
b[k] = b[i];
b[i] = xp;
for(int j=0; j<degree; j++){
xp = G[k][j];
G[k][j] = G[i][j];
G[i][j] = xp;
}
}
// Convert matrix to upper triangular form
for(int j=i+1; j<degree; j++){
xp = G[j][i]/G[i][i];
b[j] -= xp * b[i];
for(int k=0; k<degree; k++){ G[j][k] -= xp*G[i][k]; }
}
}
// Use Gaussian elimination to solve equations and calculate initial guess
chi = Zero();
r = phi;
for(int i=degree-1; i>=0; i--){
a[i] = 0.0;
for(int j=i+1; j<degree; j++){ a[i] += G[i][j] * a[j]; }
a[i] = (b[i]-a[i])/G[i][i];
chi += a[i]*v[i];
r -= a[i]*MdagMv[i];
}
RealD true_r(0.0);
ComplexD tmp;
for(int i=0; i<degree; i++){
tmp = -b[i];
for(int j=0; j<degree; j++){ tmp += G[i][j]*a[j]; }
tmp = conjugate(tmp)*tmp;
true_r += std::sqrt(tmp.real());
}
RealD error = std::sqrt(norm2(r)/norm2(phi));
std::cout << GridLogMessage << "ChronoForecast: |res|/|src| = " << error << std::endl;
return chi;
};
};
NAMESPACE_END(Grid);
#endif

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Copyright (c) 2011 Michael Clark
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/approx/MultiShiftFunction.cc
Copyright (C) 2015
Author: Azusa Yamaguchi <ayamaguc@staffmail.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#include <Grid/GridCore.h>
NAMESPACE_BEGIN(Grid);
double MultiShiftFunction::approx(double x)
{
double a = norm;
for(int n=0;n<poles.size();n++){
a = a + residues[n]/(x+poles[n]);
}
return a;
}
void MultiShiftFunction::gnuplot(std::ostream &out)
{
out<<"f(x) = "<<norm<<"";
for(int n=0;n<poles.size();n++){
out<<"+("<<residues[n]<<"/(x+"<<poles[n]<<"))";
}
out<<";"<<std::endl;
}
void MultiShiftFunction::csv(std::ostream &out)
{
for (double x=lo; x<hi; x*=1.05) {
double f = approx(x);
double r = sqrt(x);
out<< x<<","<<r<<","<<f<<","<<r-f<<std::endl;
}
return;
}
NAMESPACE_END(Grid);

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/approx/MultiShiftFunction.h
Copyright (C) 2015
Author: Azusa Yamaguchi <ayamaguc@staffmail.ed.ac.uk>
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef MULTI_SHIFT_FUNCTION
#define MULTI_SHIFT_FUNCTION
NAMESPACE_BEGIN(Grid);
class MultiShiftFunction {
public:
int order;
std::vector<RealD> poles;
std::vector<RealD> residues;
std::vector<RealD> tolerances;
RealD norm;
RealD lo,hi;
MultiShiftFunction(int n,RealD _lo,RealD _hi): poles(n), residues(n), lo(_lo), hi(_hi) {;};
RealD approx(RealD x);
void csv(std::ostream &out);
void gnuplot(std::ostream &out);
void Init(AlgRemez & remez,double tol,bool inverse)
{
order=remez.getDegree();
tolerances.resize(remez.getDegree(),tol);
poles.resize(remez.getDegree());
residues.resize(remez.getDegree());
remez.getBounds(lo,hi);
if ( inverse ) remez.getIPFE (&residues[0],&poles[0],&norm);
else remez.getPFE (&residues[0],&poles[0],&norm);
}
// Allow deferred initialisation
MultiShiftFunction(void){};
MultiShiftFunction(AlgRemez & remez,double tol,bool inverse)
{
Init(remez,tol,inverse);
}
};
NAMESPACE_END(Grid);
#endif

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-----------------------------------------------------------------------------------
PAB. Took Mike Clark's AlgRemez from GitHub and (modified a little) include.
This is open source and license and readme and comments are preserved consistent
with the license. Mike, thankyou!
-----------------------------------------------------------------------------------
-----------------------------------------------------------------------------------
AlgRemez
The archive downloadable here contains an implementation of the Remez
algorithm which calculates optimal rational (and polynomial)
approximations to the nth root over a given spectral range. The Remez
algorithm, although in principle is extremely straightforward to
program, is quite difficult to get completely correct, e.g., the Maple
implementation of the algorithm does not always converge to the
correct answer.
To use this algorithm you need to install GMP, the GNU Multiple
Precision Library, and when configuring the install, you must include
the --enable-mpfr option (see the GMP manual for more details). You
also have to edit the Makefile for AlgRemez appropriately for your
system, namely to point corrrectly to the location of the GMP library.
The simple main program included with this archive invokes the
AlgRemez class to calculate an approximation given by command line
arguments. It is invoked by the following
./test y z n d lambda_low lambda_high precision,
where the function to be approximated is f(x) = x^(y/z), with degree
(n,d) over the spectral range [lambda_low, lambda_high], using
precision digits of precision in the arithmetic. So an example would
be
./test 1 2 5 5 0.0004 64 40
which corresponds to constructing a rational approximation to the
square root function, with degree (5,5) over the range [0.0004,64]
with 40 digits of precision used for the arithmetic. The parameters y
and z must be positive, the approximation to f(x) = x^(-y/z) is simply
the inverse of the approximation to f(x) = x^(y/z). After the
approximation has been constructed, the roots and poles of the
rational function are found, and then the partial fraction expansion
of both the rational function and it's inverse are found, the results
of which are output to a file called "approx.dat". In addition, the
error function of the approximation is output to "error.dat", where it
can be checked that the resultant approximation satisfies Chebychev's
criterion, namely all error maxima are equal in magnitude, and
adjacent maxima are oppostie in sign. There are some caveats here
however, the optimal polynomial approximation has complex roots, and
the root finding implemented here cannot (yet) handle complex roots.
In addition, the partial fraction expansion of rational approximations
is only found for the case n = d, i.e., the degree of numerator
polynomial equals that of the denominator polynomial. The convention
for the partial fraction expansion is that polar shifts are always
written added to x, not subtracted.
To do list
1. Include an exponential dampening factor in the function to be
approximated. This may sound trivial to implement, but for some
parameters, the algorithm seems to breakdown. Also, the roots in the
rational approximation sometimes become complex, which currently
breaks the stupidly simple root finding code.
2. Make the algorithm faster - it's too slow when running on qcdoc.
3. Add complex root finding.
4. Add more options for error minimisation - currently the code
minimises the relative error, should add options for absolute error,
and other norms.
There will be a forthcoming publication concerning the results
generated by this software, but in the meantime, if you use this
software, please cite it as
"M.A. Clark and A.D. Kennedy, https://github.com/mikeaclark/AlgRemez, 2005".
If you have any problems using the software, then please email scientist.mike@gmail.com.

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/*
Mike Clark - 25th May 2005
alg_remez.C
AlgRemez is an implementation of the Remez algorithm, which in this
case is used for generating the optimal nth root rational
approximation.
Note this class requires the gnu multiprecision (GNU MP) library.
*/
#include<math.h>
#include<stdio.h>
#include<stdlib.h>
#include<string>
#include<iostream>
#include<iomanip>
#include<cassert>
#include<Grid/algorithms/approx/Remez.h>
// Constructor
AlgRemez::AlgRemez(double lower, double upper, long precision)
{
prec = precision;
bigfloat::setDefaultPrecision(prec);
apstrt = lower;
apend = upper;
apwidt = apend - apstrt;
std::cout<<"Approximation bounds are ["<<apstrt<<","<<apend<<"]\n";
std::cout<<"Precision of arithmetic is "<<precision<<std::endl;
alloc = 0;
n = 0;
d = 0;
foundRoots = 0;
// Only require the approximation spread to be less than 1 ulp
tolerance = 1e-15;
}
// Destructor
AlgRemez::~AlgRemez()
{
if (alloc) {
delete [] param;
delete [] roots;
delete [] poles;
delete [] xx;
delete [] mm;
delete [] a_power;
delete [] a;
}
}
// Free memory and reallocate as necessary
void AlgRemez::allocate(int num_degree, int den_degree)
{
// Arrays have previously been allocated, deallocate first, then allocate
if (alloc) {
delete [] param;
delete [] roots;
delete [] poles;
delete [] xx;
delete [] mm;
}
// Note use of new and delete in memory allocation - cannot run on qcdsp
param = new bigfloat[num_degree+den_degree+1];
roots = new bigfloat[num_degree];
poles = new bigfloat[den_degree];
xx = new bigfloat[num_degree+den_degree+3];
mm = new bigfloat[num_degree+den_degree+2];
if (!alloc) {
// The coefficients of the sum in the exponential
a = new bigfloat[SUM_MAX];
a_power = new int[SUM_MAX];
}
alloc = 1;
}
// Reset the bounds of the approximation
void AlgRemez::setBounds(double lower, double upper)
{
apstrt = lower;
apend = upper;
apwidt = apend - apstrt;
}
// Generate the rational approximation x^(pnum/pden)
double AlgRemez::generateApprox(int degree, unsigned long pnum,
unsigned long pden)
{
return generateApprox(degree, degree, pnum, pden);
}
double AlgRemez::generateApprox(int num_degree, int den_degree,
unsigned long pnum, unsigned long pden)
{
double *a_param = 0;
int *a_pow = 0;
return generateApprox(num_degree, den_degree, pnum, pden, 0, a_param, a_pow);
}
// Generate the rational approximation x^(pnum/pden)
double AlgRemez::generateApprox(int num_degree, int den_degree,
unsigned long pnum, unsigned long pden,
int a_len, double *a_param, int *a_pow)
{
std::cout<<"Degree of the approximation is ("<<num_degree<<","<<den_degree<<")\n";
std::cout<<"Approximating the function x^("<<pnum<<"/"<<pden<<")\n";
// Reallocate arrays, since degree has changed
if (num_degree != n || den_degree != d) allocate(num_degree,den_degree);
assert(a_len<=SUM_MAX);
step = new bigfloat[num_degree+den_degree+2];
a_length = a_len;
for (int j=0; j<a_len; j++) {
a[j]= a_param[j];
a_power[j] = a_pow[j];
}
power_num = pnum;
power_den = pden;
spread = 1.0e37;
iter = 0;
n = num_degree;
d = den_degree;
neq = n + d + 1;
initialGuess();
stpini(step);
while (spread > tolerance) { //iterate until convergance
if (iter++%100==0)
std::cout<<"Iteration " <<iter-1<<" spread "<<(double)spread<<" delta "<<(double)delta<<std::endl;
equations();
if (delta < tolerance) {
std::cout<<"Delta too small, try increasing precision\n";
assert(0);
};
assert( delta>= tolerance);
search(step);
}
int sign;
double error = (double)getErr(mm[0],&sign);
std::cout<<"Converged at "<<iter<<" iterations; error = "<<error<<std::endl;
// Once the approximation has been generated, calculate the roots
if(!root()) {
std::cout<<"Root finding failed\n";
} else {
foundRoots = 1;
}
delete [] step;
// Return the maximum error in the approximation
return error;
}
// Return the partial fraction expansion of the approximation x^(pnum/pden)
int AlgRemez::getPFE(double *Res, double *Pole, double *Norm) {
if (n!=d) {
std::cout<<"Cannot handle case: Numerator degree neq Denominator degree\n";
return 0;
}
if (!alloc) {
std::cout<<"Approximation not yet generated\n";
return 0;
}
if (!foundRoots) {
std::cout<<"Roots not found, so PFE cannot be taken\n";
return 0;
}
bigfloat *r = new bigfloat[n];
bigfloat *p = new bigfloat[d];
for (int i=0; i<n; i++) r[i] = roots[i];
for (int i=0; i<d; i++) p[i] = poles[i];
// Perform a partial fraction expansion
pfe(r, p, norm);
// Convert to double and return
*Norm = (double)norm;
for (int i=0; i<n; i++) Res[i] = (double)r[i];
for (int i=0; i<d; i++) Pole[i] = (double)p[i];
delete [] r;
delete [] p;
// Where the smallest shift is located
return 0;
}
// Return the partial fraction expansion of the approximation x^(-pnum/pden)
int AlgRemez::getIPFE(double *Res, double *Pole, double *Norm) {
if (n!=d) {
std::cout<<"Cannot handle case: Numerator degree neq Denominator degree\n";
return 0;
}
if (!alloc) {
std::cout<<"Approximation not yet generated\n";
return 0;
}
if (!foundRoots) {
std::cout<<"Roots not found, so PFE cannot be taken\n";
return 0;
}
bigfloat *r = new bigfloat[d];
bigfloat *p = new bigfloat[n];
// Want the inverse function
for (int i=0; i<n; i++) {
r[i] = poles[i];
p[i] = roots[i];
}
// Perform a partial fraction expansion
pfe(r, p, (bigfloat)1l/norm);
// Convert to double and return
*Norm = (double)((bigfloat)1l/(norm));
for (int i=0; i<n; i++) {
Res[i] = (double)r[i];
Pole[i] = (double)p[i];
}
delete [] r;
delete [] p;
// Where the smallest shift is located
return 0;
}
// Initial values of maximal and minimal errors
void AlgRemez::initialGuess() {
// Supply initial guesses for solution points
long ncheb = neq; // Degree of Chebyshev error estimate
bigfloat a, r;
// Find ncheb+1 extrema of Chebyshev polynomial
a = ncheb;
mm[0] = apstrt;
for (long i = 1; i < ncheb; i++) {
r = 0.5 * (1 - cos((M_PI * i)/(double) a));
//r *= sqrt_bf(r);
r = (exp((double)r)-1.0)/(exp(1.0)-1.0);
mm[i] = apstrt + r * apwidt;
}
mm[ncheb] = apend;
a = 2.0 * ncheb;
for (long i = 0; i <= ncheb; i++) {
r = 0.5 * (1 - cos(M_PI * (2*i+1)/(double) a));
//r *= sqrt_bf(r); // Squeeze to low end of interval
r = (exp((double)r)-1.0)/(exp(1.0)-1.0);
xx[i] = apstrt + r * apwidt;
}
}
// Initialise step sizes
void AlgRemez::stpini(bigfloat *step) {
xx[neq+1] = apend;
delta = 0.25;
step[0] = xx[0] - apstrt;
for (int i = 1; i < neq; i++) step[i] = xx[i] - xx[i-1];
step[neq] = step[neq-1];
}
// Search for error maxima and minima
void AlgRemez::search(bigfloat *step) {
bigfloat a, q, xm, ym, xn, yn, xx0, xx1;
int i, meq, emsign, ensign, steps;
meq = neq + 1;
bigfloat *yy = new bigfloat[meq];
bigfloat eclose = 1.0e30;
bigfloat farther = 0l;
xx0 = apstrt;
for (i = 0; i < meq; i++) {
steps = 0;
xx1 = xx[i]; // Next zero
if (i == meq-1) xx1 = apend;
xm = mm[i];
ym = getErr(xm,&emsign);
q = step[i];
xn = xm + q;
if (xn < xx0 || xn >= xx1) { // Cannot skip over adjacent boundaries
q = -q;
xn = xm;
yn = ym;
ensign = emsign;
} else {
yn = getErr(xn,&ensign);
if (yn < ym) {
q = -q;
xn = xm;
yn = ym;
ensign = emsign;
}
}
while(yn >= ym) { // March until error becomes smaller.
if (++steps > 10) break;
ym = yn;
xm = xn;
emsign = ensign;
a = xm + q;
if (a == xm || a <= xx0 || a >= xx1) break;// Must not skip over the zeros either side.
xn = a;
yn = getErr(xn,&ensign);
}
mm[i] = xm; // Position of maximum
yy[i] = ym; // Value of maximum
if (eclose > ym) eclose = ym;
if (farther < ym) farther = ym;
xx0 = xx1; // Walk to next zero.
} // end of search loop
q = (farther - eclose); // Decrease step size if error spread increased
if (eclose != 0.0) q /= eclose; // Relative error spread
if (q >= spread) delta *= 0.5; // Spread is increasing; decrease step size
spread = q;
for (i = 0; i < neq; i++) {
q = yy[i+1];
if (q != 0.0) q = yy[i] / q - (bigfloat)1l;
else q = 0.0625;
if (q > (bigfloat)0.25) q = 0.25;
q *= mm[i+1] - mm[i];
step[i] = q * delta;
}
step[neq] = step[neq-1];
for (i = 0; i < neq; i++) { // Insert new locations for the zeros.
xm = xx[i] - step[i];
if (xm <= apstrt) continue;
if (xm >= apend) continue;
if (xm <= mm[i]) xm = (bigfloat)0.5 * (mm[i] + xx[i]);
if (xm >= mm[i+1]) xm = (bigfloat)0.5 * (mm[i+1] + xx[i]);
xx[i] = xm;
}
delete [] yy;
}
// Solve the equations
void AlgRemez::equations(void) {
bigfloat x, y, z;
int i, j, ip;
bigfloat *aa;
bigfloat *AA = new bigfloat[(neq)*(neq)];
bigfloat *BB = new bigfloat[neq];
for (i = 0; i < neq; i++) { // set up the equations for solution by simq()
ip = neq * i; // offset to 1st element of this row of matrix
x = xx[i]; // the guess for this row
y = func(x); // right-hand-side vector
z = (bigfloat)1l;
aa = AA+ip;
for (j = 0; j <= n; j++) {
*aa++ = z;
z *= x;
}
z = (bigfloat)1l;
for (j = 0; j < d; j++) {
*aa++ = -y * z;
z *= x;
}
BB[i] = y * z; // Right hand side vector
}
// Solve the simultaneous linear equations.
if (simq(AA, BB, param, neq)) {
std::cout<<"simq failed\n";
exit(0);
}
delete [] AA;
delete [] BB;
}
// Evaluate the rational form P(x)/Q(x) using coefficients
// from the solution vector param
bigfloat AlgRemez::approx(const bigfloat x) {
bigfloat yn, yd;
int i;
// Work backwards toward the constant term.
yn = param[n]; // Highest order numerator coefficient
for (i = n-1; i >= 0; i--) yn = x * yn + param[i];
yd = x + param[n+d]; // Highest degree coefficient = 1.0
for (i = n+d-1; i > n; i--) yd = x * yd + param[i];
return(yn/yd);
}
// Compute size and sign of the approximation error at x
bigfloat AlgRemez::getErr(bigfloat x, int *sign) {
bigfloat e, f;
f = func(x);
e = approx(x) - f;
if (f != 0) e /= f;
if (e < (bigfloat)0.0) {
*sign = -1;
e = -e;
}
else *sign = 1;
return(e);
}
// Calculate function required for the approximation.
bigfloat AlgRemez::func(const bigfloat x) {
bigfloat z = (bigfloat)power_num / (bigfloat)power_den;
bigfloat y;
if (x == (bigfloat)1.0) y = (bigfloat)1.0;
else y = pow_bf(x,z);
if (a_length > 0) {
bigfloat sum = 0l;
for (int j=0; j<a_length; j++) sum += a[j]*pow_bf(x,a_power[j]);
return y * exp_bf(sum);
} else {
return y;
}
}
// Solve the system AX=B
int AlgRemez::simq(bigfloat A[], bigfloat B[], bigfloat X[], int n) {
int i, j, ij, ip, ipj, ipk, ipn;
int idxpiv, iback;
int k, kp, kp1, kpk, kpn;
int nip, nkp, nm1;
bigfloat em, q, rownrm, big, size, pivot, sum;
bigfloat *aa;
// simq() work vector
int *IPS = new int[(neq) * sizeof(int)];
nm1 = n - 1;
// Initialize IPS and X
ij = 0;
for (i = 0; i < n; i++) {
IPS[i] = i;
rownrm = 0.0;
for(j = 0; j < n; j++) {
q = abs_bf(A[ij]);
if(rownrm < q) rownrm = q;
++ij;
}
if (rownrm == (bigfloat)0l) {
std::cout<<"simq rownrm=0\n";
delete [] IPS;
return(1);
}
X[i] = (bigfloat)1.0 / rownrm;
}
for (k = 0; k < nm1; k++) {
big = 0.0;
idxpiv = 0;
for (i = k; i < n; i++) {
ip = IPS[i];
ipk = n*ip + k;
size = abs_bf(A[ipk]) * X[ip];
if (size > big) {
big = size;
idxpiv = i;
}
}
if (big == (bigfloat)0l) {
std::cout<<"simq big=0\n";
delete [] IPS;
return(2);
}
if (idxpiv != k) {
j = IPS[k];
IPS[k] = IPS[idxpiv];
IPS[idxpiv] = j;
}
kp = IPS[k];
kpk = n*kp + k;
pivot = A[kpk];
kp1 = k+1;
for (i = kp1; i < n; i++) {
ip = IPS[i];
ipk = n*ip + k;
em = -A[ipk] / pivot;
A[ipk] = -em;
nip = n*ip;
nkp = n*kp;
aa = A+nkp+kp1;
for (j = kp1; j < n; j++) {
ipj = nip + j;
A[ipj] = A[ipj] + em * *aa++;
}
}
}
kpn = n * IPS[n-1] + n - 1; // last element of IPS[n] th row
if (A[kpn] == (bigfloat)0l) {
std::cout<<"simq A[kpn]=0\n";
delete [] IPS;
return(3);
}
ip = IPS[0];
X[0] = B[ip];
for (i = 1; i < n; i++) {
ip = IPS[i];
ipj = n * ip;
sum = 0.0;
for (j = 0; j < i; j++) {
sum += A[ipj] * X[j];
++ipj;
}
X[i] = B[ip] - sum;
}
ipn = n * IPS[n-1] + n - 1;
X[n-1] = X[n-1] / A[ipn];
for (iback = 1; iback < n; iback++) {
//i goes (n-1),...,1
i = nm1 - iback;
ip = IPS[i];
nip = n*ip;
sum = 0.0;
aa = A+nip+i+1;
for (j= i + 1; j < n; j++)
sum += *aa++ * X[j];
X[i] = (X[i] - sum) / A[nip+i];
}
delete [] IPS;
return(0);
}
// Calculate the roots of the approximation
int AlgRemez::root() {
long i,j;
bigfloat x,dx=0.05;
bigfloat upper=1, lower=-100000;
bigfloat tol = 1e-20;
bigfloat *poly = new bigfloat[neq+1];
// First find the numerator roots
for (i=0; i<=n; i++) poly[i] = param[i];
for (i=n-1; i>=0; i--) {
roots[i] = rtnewt(poly,i+1,lower,upper,tol);
if (roots[i] == 0.0) {
std::cout<<"Failure to converge on root "<<i+1<<"/"<<n<<"\n";
return 0;
}
poly[0] = -poly[0]/roots[i];
for (j=1; j<=i; j++) poly[j] = (poly[j-1] - poly[j])/roots[i];
}
// Now find the denominator roots
poly[d] = 1l;
for (i=0; i<d; i++) poly[i] = param[n+1+i];
for (i=d-1; i>=0; i--) {
poles[i]=rtnewt(poly,i+1,lower,upper,tol);
if (poles[i] == 0.0) {
std::cout<<"Failure to converge on pole "<<i+1<<"/"<<d<<"\n";
return 0;
}
poly[0] = -poly[0]/poles[i];
for (j=1; j<=i; j++) poly[j] = (poly[j-1] - poly[j])/poles[i];
}
norm = param[n];
delete [] poly;
return 1;
}
// Evaluate the polynomial
bigfloat AlgRemez::polyEval(bigfloat x, bigfloat *poly, long size) {
bigfloat f = poly[size];
for (int i=size-1; i>=0; i--) f = f*x + poly[i];
return f;
}
// Evaluate the differential of the polynomial
bigfloat AlgRemez::polyDiff(bigfloat x, bigfloat *poly, long size) {
bigfloat df = (bigfloat)size*poly[size];
for (int i=size-1; i>0; i--) df = df*x + (bigfloat)i*poly[i];
return df;
}
// Newton's method to calculate roots
bigfloat AlgRemez::rtnewt(bigfloat *poly, long i, bigfloat x1,
bigfloat x2, bigfloat xacc) {
int j;
bigfloat df, dx, f, rtn;
rtn=(bigfloat)0.5*(x1+x2);
for (j=1; j<=JMAX;j++) {
f = polyEval(rtn, poly, i);
df = polyDiff(rtn, poly, i);
dx = f/df;
rtn -= dx;
if (abs_bf(dx) < xacc) return rtn;
}
std::cout<<"Maximum number of iterations exceeded in rtnewt\n";
return 0.0;
}
// Evaluate the partial fraction expansion of the rational function
// with res roots and poles poles. Result is overwritten on input
// arrays.
void AlgRemez::pfe(bigfloat *res, bigfloat *poles, bigfloat norm) {
int i,j,small;
bigfloat temp;
bigfloat *numerator = new bigfloat[n];
bigfloat *denominator = new bigfloat[d];
// Construct the polynomials explicitly
for (i=1; i<n; i++) {
numerator[i] = 0l;
denominator[i] = 0l;
}
numerator[0]=1l;
denominator[0]=1l;
for (j=0; j<n; j++) {
for (i=n-1; i>=0; i--) {
numerator[i] *= -res[j];
denominator[i] *= -poles[j];
if (i>0) {
numerator[i] += numerator[i-1];
denominator[i] += denominator[i-1];
}
}
}
// Convert to proper fraction form.
// Fraction is now in the form 1 + n/d, where O(n)+1=O(d)
for (i=0; i<n; i++) numerator[i] -= denominator[i];
// Find the residues of the partial fraction expansion and absorb the
// coefficients.
for (i=0; i<n; i++) {
res[i] = 0l;
for (j=n-1; j>=0; j--) {
res[i] = poles[i]*res[i]+numerator[j];
}
for (j=n-1; j>=0; j--) {
if (i!=j) res[i] /= poles[i]-poles[j];
}
res[i] *= norm;
}
// res now holds the residues
j = 0;
for (i=0; i<n; i++) poles[i] = -poles[i];
// Move the ordering of the poles from smallest to largest
for (j=0; j<n; j++) {
small = j;
for (i=j+1; i<n; i++) {
if (poles[i] < poles[small]) small = i;
}
if (small != j) {
temp = poles[small];
poles[small] = poles[j];
poles[j] = temp;
temp = res[small];
res[small] = res[j];
res[j] = temp;
}
}
delete [] numerator;
delete [] denominator;
}
double AlgRemez::evaluateApprox(double x) {
return (double)approx((bigfloat)x);
}
double AlgRemez::evaluateInverseApprox(double x) {
return 1.0/(double)approx((bigfloat)x);
}
double AlgRemez::evaluateFunc(double x) {
return (double)func((bigfloat)x);
}
double AlgRemez::evaluateInverseFunc(double x) {
return 1.0/(double)func((bigfloat)x);
}
void AlgRemez::csv(std::ostream & os)
{
double lambda_low = apstrt;
double lambda_high= apend;
for (double x=lambda_low; x<lambda_high; x*=1.05) {
double f = evaluateFunc(x);
double r = evaluateApprox(x);
os<< x<<","<<r<<","<<f<<","<<r-f<<std::endl;
}
return;
}

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/*
Mike Clark - 25th May 2005
alg_remez.h
AlgRemez is an implementation of the Remez algorithm, which in this
case is used for generating the optimal nth root rational
approximation.
Note this class requires the gnu multiprecision (GNU MP) library.
*/
#ifndef INCLUDED_ALG_REMEZ_H
#define INCLUDED_ALG_REMEZ_H
#include <stddef.h>
#include <Grid/GridStd.h>
#ifdef HAVE_LIBGMP
#include "bigfloat.h"
#else
#include "bigfloat_double.h"
#endif
#define JMAX 10000 //Maximum number of iterations of Newton's approximation
#define SUM_MAX 10 // Maximum number of terms in exponential
/*
*Usage examples
AlgRemez remez(lambda_low,lambda_high,precision);
error = remez.generateApprox(n,d,y,z);
remez.getPFE(res,pole,&norm);
remez.getIPFE(res,pole,&norm);
remez.csv(ostream &os);
*/
class AlgRemez
{
private:
char *cname;
// The approximation parameters
bigfloat *param, *roots, *poles;
bigfloat norm;
// The numerator and denominator degree (n=d)
int n, d;
// The bounds of the approximation
bigfloat apstrt, apwidt, apend;
// the numerator and denominator of the power we are approximating
unsigned long power_num;
unsigned long power_den;
// Flag to determine whether the arrays have been allocated
int alloc;
// Flag to determine whether the roots have been found
int foundRoots;
// Variables used to calculate the approximation
int nd1, iter;
bigfloat *xx, *mm, *step;
bigfloat delta, spread, tolerance;
// The exponential summation coefficients
bigfloat *a;
int *a_power;
int a_length;
// The number of equations we must solve at each iteration (n+d+1)
int neq;
// The precision of the GNU MP library
long prec;
// Initial values of maximal and minmal errors
void initialGuess();
// Solve the equations
void equations();
// Search for error maxima and minima
void search(bigfloat *step);
// Initialise step sizes
void stpini(bigfloat *step);
// Calculate the roots of the approximation
int root();
// Evaluate the polynomial
bigfloat polyEval(bigfloat x, bigfloat *poly, long size);
//complex_bf polyEval(complex_bf x, complex_bf *poly, long size);
// Evaluate the differential of the polynomial
bigfloat polyDiff(bigfloat x, bigfloat *poly, long size);
//complex_bf polyDiff(complex_bf x, complex_bf *poly, long size);
// Newton's method to calculate roots
bigfloat rtnewt(bigfloat *poly, long i, bigfloat x1, bigfloat x2, bigfloat xacc);
//complex_bf rtnewt(complex_bf *poly, long i, bigfloat x1, bigfloat x2, bigfloat xacc);
// Evaluate the partial fraction expansion of the rational function
// with res roots and poles poles. Result is overwritten on input
// arrays.
void pfe(bigfloat *res, bigfloat* poles, bigfloat norm);
// Calculate function required for the approximation
bigfloat func(bigfloat x);
// Compute size and sign of the approximation error at x
bigfloat getErr(bigfloat x, int *sign);
// Solve the system AX=B
int simq(bigfloat *A, bigfloat *B, bigfloat *X, int n);
// Free memory and reallocate as necessary
void allocate(int num_degree, int den_degree);
// Evaluate the rational form P(x)/Q(x) using coefficients from the
// solution vector param
bigfloat approx(bigfloat x);
public:
// Constructor
AlgRemez(double lower, double upper, long prec);
// Destructor
virtual ~AlgRemez();
int getDegree(void){
assert(n==d);
return n;
}
// Reset the bounds of the approximation
void setBounds(double lower, double upper);
// Reset the bounds of the approximation
void getBounds(double &lower, double &upper) {
lower=(double)apstrt;
upper=(double)apend;
}
// Generate the rational approximation x^(pnum/pden)
double generateApprox(int num_degree, int den_degree,
unsigned long power_num, unsigned long power_den,
int a_len, double* a_param, int* a_pow);
double generateApprox(int num_degree, int den_degree,
unsigned long power_num, unsigned long power_den);
double generateApprox(int degree, unsigned long power_num,
unsigned long power_den);
// Return the partial fraction expansion of the approximation x^(pnum/pden)
int getPFE(double *res, double *pole, double *norm);
// Return the partial fraction expansion of the approximation x^(-pnum/pden)
int getIPFE(double *res, double *pole, double *norm);
// Evaluate the rational form P(x)/Q(x) using coefficients from the
// solution vector param
double evaluateApprox(double x);
// Evaluate the rational form Q(x)/P(x) using coefficients from the
// solution vector param
double evaluateInverseApprox(double x);
// Calculate function required for the approximation
double evaluateFunc(double x);
// Calculate inverse function required for the approximation
double evaluateInverseFunc(double x);
// Dump csv of function, approx and error
void csv(std::ostream &os);
};
#endif // Include guard

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/* -*- Mode: C; comment-column: 22; fill-column: 79; compile-command: "gcc -o zolotarev zolotarev.c -ansi -pedantic -lm -DTEST"; -*- */
#define VERSION Source Time-stamp: <2015-05-18 16:32:08 neo>
/* These C routines evalute the optimal rational approximation to the signum
* function for epsilon < |x| < 1 using Zolotarev's theorem.
*
* To obtain reliable results for high degree approximations (large n) it is
* necessary to compute using sufficiently high precision arithmetic. To this
* end the code has been parameterised to work with the preprocessor names
* INTERNAL_PRECISION and PRECISION set to float, double, or long double as
* appropriate. INTERNAL_PRECISION is used in computing the Zolotarev
* coefficients, which are converted to PRECISION before being returned to the
* caller. Presumably even higher precision could be obtained using GMP or
* similar package, but bear in mind that rounding errors might also be
* significant in evaluating the resulting polynomial. The convergence criteria
* have been written in a precision-independent form. */
#include <math.h>
#include <stdlib.h>
#include <stdio.h>
#define MAX(a,b) ((a) > (b) ? (a) : (b))
#define MIN(a,b) ((a) < (b) ? (a) : (b))
#ifndef INTERNAL_PRECISION
#define INTERNAL_PRECISION double
#endif
#include "Zolotarev.h"
#define ZOLOTAREV_INTERNAL
#undef ZOLOTAREV_DATA
#define ZOLOTAREV_DATA izd
#undef ZPRECISION
#define ZPRECISION INTERNAL_PRECISION
#include "Zolotarev.h"
#undef ZOLOTAREV_INTERNAL
/* The ANSI standard appears not to know what pi is */
#ifndef M_PI
#define M_PI ((INTERNAL_PRECISION) 3.141592653589793238462643383279502884197\
169399375105820974944592307816406286208998628034825342117068)
#endif
#define ZERO ((INTERNAL_PRECISION) 0)
#define ONE ((INTERNAL_PRECISION) 1)
#define TWO ((INTERNAL_PRECISION) 2)
#define THREE ((INTERNAL_PRECISION) 3)
#define FOUR ((INTERNAL_PRECISION) 4)
#define HALF (ONE/TWO)
/* The following obscenity seems to be the simplest (?) way to coerce the C
* preprocessor to convert the value of a preprocessor token into a string. */
#define PP2(x) #x
#define PP1(a,b,c) a ## b(c)
#define STRINGIFY(name) PP1(PP,2,name)
/* Compute the partial fraction expansion coefficients (alpha) from the
* factored form */
NAMESPACE_BEGIN(Grid);
NAMESPACE_BEGIN(Approx);
static void construct_partfrac(izd *z) {
int dn = z -> dn, dd = z -> dd, type = z -> type;
int j, k, da = dd + 1 + type;
INTERNAL_PRECISION A = z -> A, *a = z -> a, *ap = z -> ap, *alpha;
alpha = (INTERNAL_PRECISION*) malloc(da * sizeof(INTERNAL_PRECISION));
for (j = 0; j < dd; j++)
for (k = 0, alpha[j] = A; k < dd; k++)
alpha[j] *=
(k < dn ? ap[j] - a[k] : ONE) / (k == j ? ONE : ap[j] - ap[k]);
if(type == 1) /* implicit pole at zero? */
for (k = 0, alpha[dd] = A * (dn > dd ? - a[dd] : ONE); k < dd; k++) {
alpha[dd] *= a[k] / ap[k];
alpha[k] *= (dn > dd ? ap[k] - a[dd] : ONE) / ap[k];
}
alpha[da-1] = dn == da - 1 ? A : ZERO;
z -> alpha = alpha;
z -> da = da;
return;
}
/* Convert factored polynomial into dense polynomial. The input is the overall
* factor A and the roots a[i], such that p = A product(x - a[i], i = 1..d) */
static INTERNAL_PRECISION *poly_factored_to_dense(INTERNAL_PRECISION A,
INTERNAL_PRECISION *a,
int d) {
INTERNAL_PRECISION *p;
int i, j;
p = (INTERNAL_PRECISION *) malloc((d + 2) * sizeof(INTERNAL_PRECISION));
p[0] = A;
for (i = 0; i < d; i++) {
p[i+1] = p[i];
for (j = i; j > 0; j--) p[j] = p[j-1] - a[i]*p[j];
p[0] *= - a[i];
}
return p;
}
/* Convert a rational function of the form R0(x) = x p(x^2)/q(x^2) (type 0) or
* R1(x) = p(x^2)/[x q(x^2)] (type 1) into its continued fraction
* representation. We assume that 0 <= deg(q) - deg(p) <= 1 for type 0 and 0 <=
* deg(p) - deg(q) <= 1 for type 1. On input p and q are in factored form, and
* deg(q) = dq, deg(p) = dp. The output is the continued fraction coefficients
* beta, where R(x) = beta[0] x + 1/(beta[1] x + 1/(...)).
*
* The method used is as follows. There are four cases to consider:
*
* 0.i. Type 0, deg p = deg q
*
* 0.ii. Type 0, deg p = deg q - 1
*
* 1.i. Type 1, deg p = deg q
*
* 1.ii. Type 1, deg p = deg q + 1
*
* and these are connected by two transformations:
*
* A. To obtain a continued fraction expansion of type 1 we use a single-step
* polynomial division we find beta and r(x) such that p(x) = beta x q(x) +
* r(x), with deg(r) = deg(q). This implies that p(x^2) = beta x^2 q(x^2) +
* r(x^2), and thus R1(x) = x beta + r(x^2)/(x q(x^2)) = x beta + 1/R0(x)
* with R0(x) = x q(x^2)/r(x^2).
*
* B. A continued fraction expansion of type 0 is obtained in a similar, but
* not identical, manner. We use the polynomial division algorithm to compute
* the quotient beta and the remainder r that satisfy p(x) = beta q(x) + r(x)
* with deg(r) = deg(q) - 1. We thus have x p(x^2) = x beta q(x^2) + x r(x^2),
* so R0(x) = x beta + x r(x^2)/q(x^2) = x beta + 1/R1(x) with R1(x) = q(x^2) /
* (x r(x^2)).
*
* Note that the deg(r) must be exactly deg(q) for (A) and deg(q) - 1 for (B)
* because p and q have disjoint roots all of multiplicity 1. This means that
* the division algorithm requires only a single polynomial subtraction step.
*
* The transformations between the cases form the following finite state
* automaton:
*
* +------+ +------+ +------+ +------+
* | | | | ---(A)---> | | | |
* | 0.ii | ---(B)---> | 1.ii | | 0.i | <---(A)--- | 1.i |
* | | | | <---(B)--- | | | |
* +------+ +------+ +------+ +------+
*/
static INTERNAL_PRECISION *contfrac_A(INTERNAL_PRECISION *,
INTERNAL_PRECISION *,
INTERNAL_PRECISION *,
INTERNAL_PRECISION *, int, int);
static INTERNAL_PRECISION *contfrac_B(INTERNAL_PRECISION *,
INTERNAL_PRECISION *,
INTERNAL_PRECISION *,
INTERNAL_PRECISION *, int, int);
static void construct_contfrac(izd *z){
INTERNAL_PRECISION *r, A = z -> A, *p = z -> a, *q = z -> ap;
int dp = z -> dn, dq = z -> dd, type = z -> type;
z -> db = 2 * dq + 1 + type;
z -> beta = (INTERNAL_PRECISION *)
malloc(z -> db * sizeof(INTERNAL_PRECISION));
p = poly_factored_to_dense(A, p, dp);
q = poly_factored_to_dense(ONE, q, dq);
r = (INTERNAL_PRECISION *) malloc((MAX(dp,dq) + 1) *
sizeof(INTERNAL_PRECISION));
if (type == 0) (void) contfrac_B(z -> beta, p, q, r, dp, dq);
else (void) contfrac_A(z -> beta, p, q, r, dp, dq);
free(p); free(q); free(r);
return;
}
static INTERNAL_PRECISION *contfrac_A(INTERNAL_PRECISION *beta,
INTERNAL_PRECISION *p,
INTERNAL_PRECISION *q,
INTERNAL_PRECISION *r, int dp, int dq) {
INTERNAL_PRECISION quot, *rb;
int j;
/* p(x) = x beta q(x) + r(x); dp = dq or dp = dq + 1 */
quot = dp == dq ? ZERO : p[dp] / q[dq];
r[0] = p[0];
for (j = 1; j <= dp; j++) r[j] = p[j] - quot * q[j-1];
#ifdef DEBUG
printf("%s: Continued Fraction form: deg p = %2d, deg q = %2d, beta = %g\n",
__FUNCTION__, dp, dq, (float) quot);
for (j = 0; j <= dq + 1; j++)
printf("\tp[%2d] = %14.6g\tq[%2d] = %14.6g\tr[%2d] = %14.6g\n",
j, (float) (j > dp ? ZERO : p[j]),
j, (float) (j == 0 ? ZERO : q[j-1]),
j, (float) (j == dp ? ZERO : r[j]));
#endif /* DEBUG */
*(rb = contfrac_B(beta, q, r, p, dq, dq)) = quot;
return rb + 1;
}
static INTERNAL_PRECISION *contfrac_B(INTERNAL_PRECISION *beta,
INTERNAL_PRECISION *p,
INTERNAL_PRECISION *q,
INTERNAL_PRECISION *r, int dp, int dq) {
INTERNAL_PRECISION quot, *rb;
int j;
/* p(x) = beta q(x) + r(x); dp = dq or dp = dq - 1 */
quot = dp == dq ? p[dp] / q[dq] : ZERO;
for (j = 0; j < dq; j++) r[j] = p[j] - quot * q[j];
#ifdef DEBUG
printf("%s: Continued Fraction form: deg p = %2d, deg q = %2d, beta = %g\n",
__FUNCTION__, dp, dq, (float) quot);
for (j = 0; j <= dq; j++)
printf("\tp[%2d] = %14.6g\tq[%2d] = %14.6g\tr[%2d] = %14.6g\n",
j, (float) (j > dp ? ZERO : p[j]),
j, (float) q[j],
j, (float) (j == dq ? ZERO : r[j]));
#endif /* DEBUG */
*(rb = dq > 0 ? contfrac_A(beta, q, r, p, dq, dq-1) : beta) = quot;
return rb + 1;
}
/* The global variable U is used to hold the argument u throughout the AGM
* recursion. The global variables F and K are set in the innermost
* instantiation of the recursive function AGM to the values of the elliptic
* integrals F(u,k) and K(k) respectively. They must be made thread local to
* make this code thread-safe in a multithreaded environment. */
static INTERNAL_PRECISION U, F, K; /* THREAD LOCAL */
/* Recursive implementation of Gauss' arithmetico-geometric mean, which is the
* kernel of the method used to compute the Jacobian elliptic functions
* sn(u,k), cn(u,k), and dn(u,k) with parameter k (where 0 < k < 1), as well
* as the elliptic integral F(s,k) satisfying F(sn(u,k)) = u and the complete
* elliptic integral K(k).
*
* The algorithm used is a recursive implementation of the Gauss (Landen)
* transformation.
*
* The function returns the value of sn(u,k'), where k' is the dual parameter,
* and also sets the values of the global variables F and K. The latter is
* used to determine the sign of cn(u,k').
*
* The algorithm is deemed to have converged when b ceases to increase. This
* works whatever INTERNAL_PRECISION is specified. */
static INTERNAL_PRECISION AGM(INTERNAL_PRECISION a,
INTERNAL_PRECISION b,
INTERNAL_PRECISION s) {
static INTERNAL_PRECISION pb = -ONE;
INTERNAL_PRECISION c, d, xi;
if (b <= pb) {
pb = -ONE;
F = asin(s) / a; /* Here, a is the AGM */
K = M_PI / (TWO * a);
return sin(U * a);
}
pb = b;
c = a - b;
d = a + b;
xi = AGM(HALF*d, sqrt(a*b), ONE + c*c == ONE ?
HALF*s*d/a : (a - sqrt(a*a - s*s*c*d))/(c*s));
return 2*a*xi / (d + c*xi*xi);
}
/* Computes sn(u,k), cn(u,k), dn(u,k), F(u,k), and K(k). It is essentially a
* wrapper for the routine AGM. The sign of cn(u,k) is defined to be -1 if
* K(k) < u < 3*K(k) and +1 otherwise, and thus sign is computed by some quite
* unnecessarily obfuscated bit manipulations. */
static void sncndnFK(INTERNAL_PRECISION u, INTERNAL_PRECISION k,
INTERNAL_PRECISION* sn, INTERNAL_PRECISION* cn,
INTERNAL_PRECISION* dn, INTERNAL_PRECISION* elF,
INTERNAL_PRECISION* elK) {
int sgn;
U = u;
*sn = AGM(ONE, sqrt(ONE - k*k), u);
sgn = ((int) (fabs(u) / K)) % 4; /* sgn = 0, 1, 2, 3 */
sgn ^= sgn >> 1; /* (sgn & 1) = 0, 1, 1, 0 */
sgn = 1 - ((sgn & 1) << 1); /* sgn = 1, -1, -1, 1 */
*cn = ((INTERNAL_PRECISION) sgn) * sqrt(ONE - *sn * *sn);
*dn = sqrt(ONE - k*k* *sn * *sn);
*elF = F;
*elK = K;
}
/* Compute the coefficients for the optimal rational approximation R(x) to
* sgn(x) of degree n over the interval epsilon < |x| < 1 using Zolotarev's
* formula.
*
* Set type = 0 for the Zolotarev approximation, which is zero at x = 0, and
* type = 1 for the approximation which is infinite at x = 0. */
zolotarev_data* zolotarev(PRECISION epsilon, int n, int type) {
INTERNAL_PRECISION A, c, cp, kp, ksq, sn, cn, dn, Kp, Kj, z, z0, t, M, F,
l, invlambda, xi, xisq, *tv, s, opl;
int m, czero, ts;
zolotarev_data *zd;
izd *d = (izd*) malloc(sizeof(izd));
d -> type = type;
d -> epsilon = (INTERNAL_PRECISION) epsilon;
d -> n = n;
d -> dd = n / 2;
d -> dn = d -> dd - 1 + n % 2; /* n even: dn = dd - 1, n odd: dn = dd */
d -> deg_denom = 2 * d -> dd;
d -> deg_num = 2 * d -> dn + 1;
d -> a = (INTERNAL_PRECISION*) malloc((1 + d -> dn) *
sizeof(INTERNAL_PRECISION));
d -> ap = (INTERNAL_PRECISION*) malloc(d -> dd *
sizeof(INTERNAL_PRECISION));
ksq = d -> epsilon * d -> epsilon;
kp = sqrt(ONE - ksq);
sncndnFK(ZERO, kp, &sn, &cn, &dn, &F, &Kp); /* compute Kp = K(kp) */
z0 = TWO * Kp / (INTERNAL_PRECISION) n;
M = ONE;
A = ONE / d -> epsilon;
sncndnFK(HALF * z0, kp, &sn, &cn, &dn, &F, &Kj); /* compute xi */
xi = ONE / dn;
xisq = xi * xi;
invlambda = xi;
for (m = 0; m < d -> dd; m++) {
czero = 2 * (m + 1) == n; /* n even and m = dd -1 */
z = z0 * ((INTERNAL_PRECISION) m + ONE);
sncndnFK(z, kp, &sn, &cn, &dn, &F, &Kj);
t = cn / sn;
c = - t*t;
if (!czero) (d -> a)[d -> dn - 1 - m] = ksq / c;
z = z0 * ((INTERNAL_PRECISION) m + HALF);
sncndnFK(z, kp, &sn, &cn, &dn, &F, &Kj);
t = cn / sn;
cp = - t*t;
(d -> ap)[d -> dd - 1 - m] = ksq / cp;
M *= (ONE - c) / (ONE - cp);
A *= (czero ? -ksq : c) * (ONE - cp) / (cp * (ONE - c));
invlambda *= (ONE - c*xisq) / (ONE - cp*xisq);
}
invlambda /= M;
d -> A = TWO / (ONE + invlambda) * A;
d -> Delta = (invlambda - ONE) / (invlambda + ONE);
d -> gamma = (INTERNAL_PRECISION*) malloc((1 + d -> n) *
sizeof(INTERNAL_PRECISION));
l = ONE / invlambda;
opl = ONE + l;
sncndnFK(sqrt( d -> type == 1
? (THREE + l) / (FOUR * opl)
: (ONE + THREE*l) / (opl*opl*opl)
), sqrt(ONE - l*l), &sn, &cn, &dn, &F, &Kj);
s = M * F;
for (m = 0; m < d -> n; m++) {
sncndnFK(s + TWO*Kp*m/n, kp, &sn, &cn, &dn, &F, &Kj);
d -> gamma[m] = d -> epsilon / dn;
}
/* If R(x) is a Zolotarev rational approximation of degree (n,m) with maximum
* error Delta, then (1 - Delta^2) / R(x) is also an optimal Chebyshev
* approximation of degree (m,n) */
if (d -> type == 1) {
d -> A = (ONE - d -> Delta * d -> Delta) / d -> A;
tv = d -> a; d -> a = d -> ap; d -> ap = tv;
ts = d -> dn; d -> dn = d -> dd; d -> dd = ts;
ts = d -> deg_num; d -> deg_num = d -> deg_denom; d -> deg_denom = ts;
}
construct_partfrac(d);
construct_contfrac(d);
/* Converting everything to PRECISION for external use only */
zd = (zolotarev_data*) malloc(sizeof(zolotarev_data));
zd -> A = (PRECISION) d -> A;
zd -> Delta = (PRECISION) d -> Delta;
zd -> epsilon = (PRECISION) d -> epsilon;
zd -> n = d -> n;
zd -> type = d -> type;
zd -> dn = d -> dn;
zd -> dd = d -> dd;
zd -> da = d -> da;
zd -> db = d -> db;
zd -> deg_num = d -> deg_num;
zd -> deg_denom = d -> deg_denom;
zd -> a = (PRECISION*) malloc(zd -> dn * sizeof(PRECISION));
for (m = 0; m < zd -> dn; m++) zd -> a[m] = (PRECISION) d -> a[m];
free(d -> a);
zd -> ap = (PRECISION*) malloc(zd -> dd * sizeof(PRECISION));
for (m = 0; m < zd -> dd; m++) zd -> ap[m] = (PRECISION) d -> ap[m];
free(d -> ap);
zd -> alpha = (PRECISION*) malloc(zd -> da * sizeof(PRECISION));
for (m = 0; m < zd -> da; m++) zd -> alpha[m] = (PRECISION) d -> alpha[m];
free(d -> alpha);
zd -> beta = (PRECISION*) malloc(zd -> db * sizeof(PRECISION));
for (m = 0; m < zd -> db; m++) zd -> beta[m] = (PRECISION) d -> beta[m];
free(d -> beta);
zd -> gamma = (PRECISION*) malloc(zd -> n * sizeof(PRECISION));
for (m = 0; m < zd -> n; m++) zd -> gamma[m] = (PRECISION) d -> gamma[m];
free(d -> gamma);
free(d);
return zd;
}
void zolotarev_free(zolotarev_data *zdata)
{
free(zdata -> a);
free(zdata -> ap);
free(zdata -> alpha);
free(zdata -> beta);
free(zdata -> gamma);
free(zdata);
}
zolotarev_data* higham(PRECISION epsilon, int n) {
INTERNAL_PRECISION A, M, c, cp, z, z0, t, epssq;
int m, czero;
zolotarev_data *zd;
izd *d = (izd*) malloc(sizeof(izd));
d -> type = 0;
d -> epsilon = (INTERNAL_PRECISION) epsilon;
d -> n = n;
d -> dd = n / 2;
d -> dn = d -> dd - 1 + n % 2; /* n even: dn = dd - 1, n odd: dn = dd */
d -> deg_denom = 2 * d -> dd;
d -> deg_num = 2 * d -> dn + 1;
d -> a = (INTERNAL_PRECISION*) malloc((1 + d -> dn) *
sizeof(INTERNAL_PRECISION));
d -> ap = (INTERNAL_PRECISION*) malloc(d -> dd *
sizeof(INTERNAL_PRECISION));
A = (INTERNAL_PRECISION) n;
z0 = M_PI / A;
A = n % 2 == 0 ? A : ONE / A;
M = d -> epsilon * A;
epssq = d -> epsilon * d -> epsilon;
for (m = 0; m < d -> dd; m++) {
czero = 2 * (m + 1) == n; /* n even and m = dd - 1*/
if (!czero) {
z = z0 * ((INTERNAL_PRECISION) m + ONE);
t = tan(z);
c = - t*t;
(d -> a)[d -> dn - 1 - m] = c;
M *= epssq - c;
}
z = z0 * ((INTERNAL_PRECISION) m + HALF);
t = tan(z);
cp = - t*t;
(d -> ap)[d -> dd - 1 - m] = cp;
M /= epssq - cp;
}
d -> gamma = (INTERNAL_PRECISION*) malloc((1 + d -> n) *
sizeof(INTERNAL_PRECISION));
for (m = 0; m < d -> n; m++) d -> gamma[m] = ONE;
d -> A = A;
d -> Delta = ONE - M;
construct_partfrac(d);
construct_contfrac(d);
/* Converting everything to PRECISION for external use only */
zd = (zolotarev_data*) malloc(sizeof(zolotarev_data));
zd -> A = (PRECISION) d -> A;
zd -> Delta = (PRECISION) d -> Delta;
zd -> epsilon = (PRECISION) d -> epsilon;
zd -> n = d -> n;
zd -> type = d -> type;
zd -> dn = d -> dn;
zd -> dd = d -> dd;
zd -> da = d -> da;
zd -> db = d -> db;
zd -> deg_num = d -> deg_num;
zd -> deg_denom = d -> deg_denom;
zd -> a = (PRECISION*) malloc(zd -> dn * sizeof(PRECISION));
for (m = 0; m < zd -> dn; m++) zd -> a[m] = (PRECISION) d -> a[m];
free(d -> a);
zd -> ap = (PRECISION*) malloc(zd -> dd * sizeof(PRECISION));
for (m = 0; m < zd -> dd; m++) zd -> ap[m] = (PRECISION) d -> ap[m];
free(d -> ap);
zd -> alpha = (PRECISION*) malloc(zd -> da * sizeof(PRECISION));
for (m = 0; m < zd -> da; m++) zd -> alpha[m] = (PRECISION) d -> alpha[m];
free(d -> alpha);
zd -> beta = (PRECISION*) malloc(zd -> db * sizeof(PRECISION));
for (m = 0; m < zd -> db; m++) zd -> beta[m] = (PRECISION) d -> beta[m];
free(d -> beta);
zd -> gamma = (PRECISION*) malloc(zd -> n * sizeof(PRECISION));
for (m = 0; m < zd -> n; m++) zd -> gamma[m] = (PRECISION) d -> gamma[m];
free(d -> gamma);
free(d);
return zd;
}
NAMESPACE_END(Approx);
NAMESPACE_END(Grid);
#ifdef TEST
#undef ZERO
#define ZERO ((PRECISION) 0)
#undef ONE
#define ONE ((PRECISION) 1)
#undef TWO
#define TWO ((PRECISION) 2)
/* Evaluate the rational approximation R(x) using the factored form */
static PRECISION zolotarev_eval(PRECISION x, zolotarev_data* rdata) {
int m;
PRECISION R;
if (rdata -> type == 0) {
R = rdata -> A * x;
for (m = 0; m < rdata -> deg_denom/2; m++)
R *= (2*(m+1) > rdata -> deg_num ? ONE : x*x - rdata -> a[m]) /
(x*x - rdata -> ap[m]);
} else {
R = rdata -> A / x;
for (m = 0; m < rdata -> deg_num/2; m++)
R *= (x*x - rdata -> a[m]) /
(2*(m+1) > rdata -> deg_denom ? ONE : x*x - rdata -> ap[m]);
}
return R;
}
/* Evaluate the rational approximation R(x) using the partial fraction form */
static PRECISION zolotarev_partfrac_eval(PRECISION x, zolotarev_data* rdata) {
int m;
PRECISION R = rdata -> alpha[rdata -> da - 1];
for (m = 0; m < rdata -> dd; m++)
R += rdata -> alpha[m] / (x * x - rdata -> ap[m]);
if (rdata -> type == 1) R += rdata -> alpha[rdata -> dd] / (x * x);
return R * x;
}
/* Evaluate the rational approximation R(x) using continued fraction form.
*
* If x = 0 and type = 1 then the result should be INF, whereas if x = 0 and
* type = 0 then the result should be 0, but division by zero will occur at
* intermediate stages of the evaluation. For IEEE implementations with
* non-signalling overflow this will work correctly since 1/(1/0) = 1/INF = 0,
* but with signalling overflow you will get an error message. */
static PRECISION zolotarev_contfrac_eval(PRECISION x, zolotarev_data* rdata) {
int m;
PRECISION R = rdata -> beta[0] * x;
for (m = 1; m < rdata -> db; m++) R = rdata -> beta[m] * x + ONE / R;
return R;
}
/* Evaluate the rational approximation R(x) using Cayley form */
static PRECISION zolotarev_cayley_eval(PRECISION x, zolotarev_data* rdata) {
int m;
PRECISION T;
T = rdata -> type == 0 ? ONE : -ONE;
for (m = 0; m < rdata -> n; m++)
T *= (rdata -> gamma[m] - x) / (rdata -> gamma[m] + x);
return (ONE - T) / (ONE + T);
}
/* Test program. Apart from printing out the parameters for R(x) it produces
* the following data files for plotting (unless NPLOT is defined):
*
* zolotarev-fn is a plot of R(x) for |x| < 1.2. This should look like sgn(x).
*
* zolotarev-err is a plot of the error |R(x) - sgn(x)| scaled by 1/Delta. This
* should oscillate deg_num + deg_denom + 2 times between +1 and -1 over the
* domain epsilon <= |x| <= 1.
*
* If ALLPLOTS is defined then zolotarev-partfrac (zolotarev-contfrac) is a
* plot of the difference between the values of R(x) computed using the
* factored form and the partial fraction (continued fraction) form, scaled by
* 1/Delta. It should be zero everywhere. */
int main(int argc, char** argv) {
int m, n, plotpts = 5000, type = 0;
float eps, x, ypferr, ycferr, ycaylerr, maxypferr, maxycferr, maxycaylerr;
zolotarev_data *rdata;
PRECISION y;
FILE *plot_function, *plot_error,
*plot_partfrac, *plot_contfrac, *plot_cayley;
if (argc < 3 || argc > 4) {
fprintf(stderr, "Usage: %s epsilon n [type]\n", *argv);
exit(EXIT_FAILURE);
}
sscanf(argv[1], "%g", &eps); /* First argument is epsilon */
sscanf(argv[2], "%d", &n); /* Second argument is n */
if (argc == 4) sscanf(argv[3], "%d", &type); /* Third argument is type */
if (type < 0 || type > 2) {
fprintf(stderr, "%s: type must be 0 (Zolotarev R(0) = 0),\n"
"\t\t1 (Zolotarev R(0) = Inf, or 2 (Higham)\n", *argv);
exit(EXIT_FAILURE);
}
rdata = type == 2
? higham((PRECISION) eps, n)
: zolotarev((PRECISION) eps, n, type);
printf("Zolotarev Test: R(epsilon = %g, n = %d, type = %d)\n\t"
STRINGIFY(VERSION) "\n\t" STRINGIFY(HVERSION)
"\n\tINTERNAL_PRECISION = " STRINGIFY(INTERNAL_PRECISION)
"\tPRECISION = " STRINGIFY(PRECISION)
"\n\n\tRational approximation of degree (%d,%d), %s at x = 0\n"
"\tDelta = %g (maximum error)\n\n"
"\tA = %g (overall factor)\n",
(float) rdata -> epsilon, rdata -> n, type,
rdata -> deg_num, rdata -> deg_denom,
rdata -> type == 1 ? "infinite" : "zero",
(float) rdata -> Delta, (float) rdata -> A);
for (m = 0; m < MIN(rdata -> dd, rdata -> dn); m++)
printf("\ta[%2d] = %14.8g\t\ta'[%2d] = %14.8g\n",
m + 1, (float) rdata -> a[m], m + 1, (float) rdata -> ap[m]);
if (rdata -> dd > rdata -> dn)
printf("\t\t\t\t\ta'[%2d] = %14.8g\n",
rdata -> dn + 1, (float) rdata -> ap[rdata -> dn]);
if (rdata -> dd < rdata -> dn)
printf("\ta[%2d] = %14.8g\n",
rdata -> dd + 1, (float) rdata -> a[rdata -> dd]);
printf("\n\tPartial fraction coefficients\n");
printf("\talpha[ 0] = %14.8g\n",
(float) rdata -> alpha[rdata -> da - 1]);
for (m = 0; m < rdata -> dd; m++)
printf("\talpha[%2d] = %14.8g\ta'[%2d] = %14.8g\n",
m + 1, (float) rdata -> alpha[m], m + 1, (float) rdata -> ap[m]);
if (rdata -> type == 1)
printf("\talpha[%2d] = %14.8g\ta'[%2d] = %14.8g\n",
rdata -> dd + 1, (float) rdata -> alpha[rdata -> dd],
rdata -> dd + 1, (float) ZERO);
printf("\n\tContinued fraction coefficients\n");
for (m = 0; m < rdata -> db; m++)
printf("\tbeta[%2d] = %14.8g\n", m, (float) rdata -> beta[m]);
printf("\n\tCayley transform coefficients\n");
for (m = 0; m < rdata -> n; m++)
printf("\tgamma[%2d] = %14.8g\n", m, (float) rdata -> gamma[m]);
#ifndef NPLOT
plot_function = fopen("zolotarev-fn.dat", "w");
plot_error = fopen("zolotarev-err.dat", "w");
#ifdef ALLPLOTS
plot_partfrac = fopen("zolotarev-partfrac.dat", "w");
plot_contfrac = fopen("zolotarev-contfrac.dat", "w");
plot_cayley = fopen("zolotarev-cayley.dat", "w");
#endif /* ALLPLOTS */
for (m = 0, maxypferr = maxycferr = maxycaylerr = 0.0; m <= plotpts; m++) {
x = 2.4 * (float) m / plotpts - 1.2;
if (rdata -> type == 0 || fabs(x) * (float) plotpts > 1.0) {
/* skip x = 0 for type 1, as R(0) is singular */
y = zolotarev_eval((PRECISION) x, rdata);
fprintf(plot_function, "%g %g\n", x, (float) y);
fprintf(plot_error, "%g %g\n",
x, (float)((y - ((x > 0.0 ? ONE : -ONE))) / rdata -> Delta));
ypferr = (float)((zolotarev_partfrac_eval((PRECISION) x, rdata) - y)
/ rdata -> Delta);
ycferr = (float)((zolotarev_contfrac_eval((PRECISION) x, rdata) - y)
/ rdata -> Delta);
ycaylerr = (float)((zolotarev_cayley_eval((PRECISION) x, rdata) - y)
/ rdata -> Delta);
if (fabs(x) < 1.0 && fabs(x) > rdata -> epsilon) {
maxypferr = MAX(maxypferr, fabs(ypferr));
maxycferr = MAX(maxycferr, fabs(ycferr));
maxycaylerr = MAX(maxycaylerr, fabs(ycaylerr));
}
#ifdef ALLPLOTS
fprintf(plot_partfrac, "%g %g\n", x, ypferr);
fprintf(plot_contfrac, "%g %g\n", x, ycferr);
fprintf(plot_cayley, "%g %g\n", x, ycaylerr);
#endif /* ALLPLOTS */
}
}
#ifdef ALLPLOTS
fclose(plot_cayley);
fclose(plot_contfrac);
fclose(plot_partfrac);
#endif /* ALLPLOTS */
fclose(plot_error);
fclose(plot_function);
printf("\n\tMaximum PF error = %g (relative to Delta)\n", maxypferr);
printf("\tMaximum CF error = %g (relative to Delta)\n", maxycferr);
printf("\tMaximum Cayley error = %g (relative to Delta)\n", maxycaylerr);
#endif /* NPLOT */
free(rdata -> a);
free(rdata -> ap);
free(rdata -> alpha);
free(rdata -> beta);
free(rdata -> gamma);
free(rdata);
return EXIT_SUCCESS;
}
#endif /* TEST */

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/* -*- Mode: C; comment-column: 22; fill-column: 79; -*- */
#ifdef __cplusplus
#include <Grid/Namespace.h>
NAMESPACE_BEGIN(Grid);
NAMESPACE_BEGIN(Approx);
#endif
#define HVERSION Header Time-stamp: <14-OCT-2004 09:26:51.00 adk@MISSCONTRARY>
#ifndef ZOLOTAREV_INTERNAL
#ifndef PRECISION
#define PRECISION double
#endif
#define ZPRECISION PRECISION
#define ZOLOTAREV_DATA zolotarev_data
#endif
/* This struct contains the coefficients which parameterise an optimal rational
* approximation to the signum function.
*
* The parameterisations used are:
*
* Factored form for type 0 (R0(0) = 0)
*
* R0(x) = A * x * prod(x^2 - a[j], j = 0 .. dn-1) / prod(x^2 - ap[j], j = 0
* .. dd-1),
*
* where deg_num = 2*dn + 1 and deg_denom = 2*dd.
*
* Factored form for type 1 (R1(0) = infinity)
*
* R1(x) = (A / x) * prod(x^2 - a[j], j = 0 .. dn-1) / prod(x^2 - ap[j], j = 0
* .. dd-1),
*
* where deg_num = 2*dn and deg_denom = 2*dd + 1.
*
* Partial fraction form
*
* R(x) = alpha[da] * x + sum(alpha[j] * x / (x^2 - ap[j]), j = 0 .. da-1)
*
* where da = dd for type 0 and da = dd + 1 with ap[dd] = 0 for type 1.
*
* Continued fraction form
*
* R(x) = beta[db-1] * x + 1 / (beta[db-2] * x + 1 / (beta[db-3] * x + ...))
*
* with the final coefficient being beta[0], with d' = 2 * dd + 1 for type 0
* and db = 2 * dd + 2 for type 1.
*
* Cayley form (Chiu's domain wall formulation)
*
* R(x) = (1 - T(x)) / (1 + T(x))
*
* where T(x) = prod((x - gamma[j]) / (x + gamma[j]), j = 0 .. n-1)
*/
typedef struct {
ZPRECISION *a, /* zeros of numerator, a[0 .. dn-1] */
*ap, /* poles (zeros of denominator), ap[0 .. dd-1] */
A, /* overall factor */
*alpha, /* coefficients of partial fraction, alpha[0 .. da-1] */
*beta, /* coefficients of continued fraction, beta[0 .. db-1] */
*gamma, /* zeros of numerator of T in Cayley form */
Delta, /* maximum error, |R(x) - sgn(x)| <= Delta */
epsilon; /* minimum x value, epsilon < |x| < 1 */
int n, /* approximation degree */
type, /* 0: R(0) = 0, 1: R(0) = infinity */
dn, dd, da, db, /* number of elements of a, ap, alpha, and beta */
deg_num, /* degree of numerator = deg_denom +/- 1 */
deg_denom; /* degree of denominator */
} ZOLOTAREV_DATA;
#ifndef ZOLOTAREV_INTERNAL
/* zolotarev(epsilon, n, type) returns a pointer to an initialised
* zolotarev_data structure. The arguments must satisfy the constraints that
* epsilon > 0, n > 0, and type = 0 or 1. */
ZOLOTAREV_DATA* higham(PRECISION epsilon, int n) ;
ZOLOTAREV_DATA* zolotarev(PRECISION epsilon, int n, int type);
void zolotarev_free(zolotarev_data *zdata);
#endif
#ifdef __cplusplus
NAMESPACE_END(Approx);
NAMESPACE_END(Grid);
#endif

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/*
Mike Clark - 25th May 2005
bigfloat.h
Simple C++ wrapper for multiprecision datatype used by AlgRemez
algorithm
*/
#ifndef INCLUDED_BIGFLOAT_H
#define INCLUDED_BIGFLOAT_H
#include <gmp.h>
#include <mpf2mpfr.h>
#include <mpfr.h>
class bigfloat {
private:
mpf_t x;
public:
bigfloat() { mpf_init(x); }
bigfloat(const bigfloat& y) { mpf_init_set(x, y.x); }
bigfloat(const unsigned long u) { mpf_init_set_ui(x, u); }
bigfloat(const long i) { mpf_init_set_si(x, i); }
bigfloat(const int i) {mpf_init_set_si(x,(long)i);}
bigfloat(const float d) { mpf_init_set_d(x, (double)d); }
bigfloat(const double d) { mpf_init_set_d(x, d); }
bigfloat(const char *str) { mpf_init_set_str(x, (char*)str, 10); }
~bigfloat(void) { mpf_clear(x); }
operator double (void) const { return (double)mpf_get_d(x); }
static void setDefaultPrecision(unsigned long dprec) {
unsigned long bprec = (unsigned long)(3.321928094 * (double)dprec);
mpf_set_default_prec(bprec);
}
void setPrecision(unsigned long dprec) {
unsigned long bprec = (unsigned long)(3.321928094 * (double)dprec);
mpf_set_prec(x,bprec);
}
unsigned long getPrecision(void) const { return mpf_get_prec(x); }
unsigned long getDefaultPrecision(void) const { return mpf_get_default_prec(); }
bigfloat& operator=(const bigfloat& y) {
mpf_set(x, y.x);
return *this;
}
bigfloat& operator=(const unsigned long y) {
mpf_set_ui(x, y);
return *this;
}
bigfloat& operator=(const signed long y) {
mpf_set_si(x, y);
return *this;
}
bigfloat& operator=(const float y) {
mpf_set_d(x, (double)y);
return *this;
}
bigfloat& operator=(const double y) {
mpf_set_d(x, y);
return *this;
}
size_t write(void);
size_t read(void);
/* Arithmetic Functions */
bigfloat& operator+=(const bigfloat& y) { return *this = *this + y; }
bigfloat& operator-=(const bigfloat& y) { return *this = *this - y; }
bigfloat& operator*=(const bigfloat& y) { return *this = *this * y; }
bigfloat& operator/=(const bigfloat& y) { return *this = *this / y; }
friend bigfloat operator+(const bigfloat& x, const bigfloat& y) {
bigfloat a;
mpf_add(a.x,x.x,y.x);
return a;
}
friend bigfloat operator+(const bigfloat& x, const unsigned long y) {
bigfloat a;
mpf_add_ui(a.x,x.x,y);
return a;
}
friend bigfloat operator-(const bigfloat& x, const bigfloat& y) {
bigfloat a;
mpf_sub(a.x,x.x,y.x);
return a;
}
friend bigfloat operator-(const unsigned long x, const bigfloat& y) {
bigfloat a;
mpf_ui_sub(a.x,x,y.x);
return a;
}
friend bigfloat operator-(const bigfloat& x, const unsigned long y) {
bigfloat a;
mpf_sub_ui(a.x,x.x,y);
return a;
}
friend bigfloat operator-(const bigfloat& x) {
bigfloat a;
mpf_neg(a.x,x.x);
return a;
}
friend bigfloat operator*(const bigfloat& x, const bigfloat& y) {
bigfloat a;
mpf_mul(a.x,x.x,y.x);
return a;
}
friend bigfloat operator*(const bigfloat& x, const unsigned long y) {
bigfloat a;
mpf_mul_ui(a.x,x.x,y);
return a;
}
friend bigfloat operator/(const bigfloat& x, const bigfloat& y){
bigfloat a;
mpf_div(a.x,x.x,y.x);
return a;
}
friend bigfloat operator/(const unsigned long x, const bigfloat& y){
bigfloat a;
mpf_ui_div(a.x,x,y.x);
return a;
}
friend bigfloat operator/(const bigfloat& x, const unsigned long y){
bigfloat a;
mpf_div_ui(a.x,x.x,y);
return a;
}
friend bigfloat sqrt_bf(const bigfloat& x){
bigfloat a;
mpf_sqrt(a.x,x.x);
return a;
}
friend bigfloat sqrt_bf(const unsigned long x){
bigfloat a;
mpf_sqrt_ui(a.x,x);
return a;
}
friend bigfloat abs_bf(const bigfloat& x){
bigfloat a;
mpf_abs(a.x,x.x);
return a;
}
friend bigfloat pow_bf(const bigfloat& a, long power) {
bigfloat b;
mpf_pow_ui(b.x,a.x,power);
return b;
}
friend bigfloat pow_bf(const bigfloat& a, bigfloat &power) {
bigfloat b;
mpfr_pow(b.x,a.x,power.x,GMP_RNDN);
return b;
}
friend bigfloat exp_bf(const bigfloat& a) {
bigfloat b;
mpfr_exp(b.x,a.x,GMP_RNDN);
return b;
}
/* Comparison Functions */
friend int operator>(const bigfloat& x, const bigfloat& y) {
int test;
test = mpf_cmp(x.x,y.x);
if (test > 0) return 1;
else return 0;
}
friend int operator<(const bigfloat& x, const bigfloat& y) {
int test;
test = mpf_cmp(x.x,y.x);
if (test < 0) return 1;
else return 0;
}
friend int sgn(const bigfloat&);
};
#endif

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/approx/bigfloat_double.h
Copyright (C) 2015
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#include <math.h>
typedef double mfloat;
class bigfloat {
private:
mfloat x;
public:
bigfloat() { }
bigfloat(const bigfloat& y) { x=y.x; }
bigfloat(const unsigned long u) { x=u; }
bigfloat(const long i) { x=i; }
bigfloat(const int i) { x=i;}
bigfloat(const float d) { x=d;}
bigfloat(const double d) { x=d;}
bigfloat(const char *str) { x=std::stod(std::string(str));}
~bigfloat(void) { }
operator double (void) const { return (double)x; }
static void setDefaultPrecision(unsigned long dprec) {
}
void setPrecision(unsigned long dprec) {
}
unsigned long getPrecision(void) const { return 64; }
unsigned long getDefaultPrecision(void) const { return 64; }
bigfloat& operator=(const bigfloat& y) { x=y.x; return *this; }
bigfloat& operator=(const unsigned long y) { x=y; return *this; }
bigfloat& operator=(const signed long y) { x=y; return *this; }
bigfloat& operator=(const float y) { x=y; return *this; }
bigfloat& operator=(const double y) { x=y; return *this; }
size_t write(void);
size_t read(void);
/* Arithmetic Functions */
bigfloat& operator+=(const bigfloat& y) { return *this = *this + y; }
bigfloat& operator-=(const bigfloat& y) { return *this = *this - y; }
bigfloat& operator*=(const bigfloat& y) { return *this = *this * y; }
bigfloat& operator/=(const bigfloat& y) { return *this = *this / y; }
friend bigfloat operator+(const bigfloat& x, const bigfloat& y) {
bigfloat a;
a.x=x.x+y.x;
return a;
}
friend bigfloat operator+(const bigfloat& x, const unsigned long y) {
bigfloat a;
a.x=x.x+y;
return a;
}
friend bigfloat operator-(const bigfloat& x, const bigfloat& y) {
bigfloat a;
a.x=x.x-y.x;
return a;
}
friend bigfloat operator-(const unsigned long x, const bigfloat& y) {
bigfloat bx(x);
return bx-y;
}
friend bigfloat operator-(const bigfloat& x, const unsigned long y) {
bigfloat by(y);
return x-by;
}
friend bigfloat operator-(const bigfloat& x) {
bigfloat a;
a.x=-x.x;
return a;
}
friend bigfloat operator*(const bigfloat& x, const bigfloat& y) {
bigfloat a;
a.x=x.x*y.x;
return a;
}
friend bigfloat operator*(const bigfloat& x, const unsigned long y) {
bigfloat a;
a.x=x.x*y;
return a;
}
friend bigfloat operator/(const bigfloat& x, const bigfloat& y){
bigfloat a;
a.x=x.x/y.x;
return a;
}
friend bigfloat operator/(const unsigned long x, const bigfloat& y){
bigfloat bx(x);
return bx/y;
}
friend bigfloat operator/(const bigfloat& x, const unsigned long y){
bigfloat by(y);
return x/by;
}
friend bigfloat sqrt_bf(const bigfloat& x){
bigfloat a;
a.x= sqrt(x.x);
return a;
}
friend bigfloat sqrt_bf(const unsigned long x){
bigfloat a(x);
return sqrt_bf(a);
}
friend bigfloat abs_bf(const bigfloat& x){
bigfloat a;
a.x=fabs(x.x);
return a;
}
friend bigfloat pow_bf(const bigfloat& a, long power) {
bigfloat b;
b.x=pow(a.x,power);
return b;
}
friend bigfloat pow_bf(const bigfloat& a, bigfloat &power) {
bigfloat b;
b.x=pow(a.x,power.x);
return b;
}
friend bigfloat exp_bf(const bigfloat& a) {
bigfloat b;
b.x=exp(a.x);
return b;
}
/* Comparison Functions */
friend int operator>(const bigfloat& x, const bigfloat& y) {
return x.x>y.x;
}
friend int operator<(const bigfloat& x, const bigfloat& y) {
return x.x<y.x;
}
friend int sgn(const bigfloat& x) {
if ( x.x>=0 ) return 1;
else return 0;
}
/* Miscellaneous Functions */
// friend bigfloat& random(void);
};

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/AdefGeneric.h
Copyright (C) 2015
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_ALGORITHMS_ITERATIVE_GENERIC_PCG
#define GRID_ALGORITHMS_ITERATIVE_GENERIC_PCG
/*
* Compared to Tang-2009: P=Pleft. P^T = PRight Q=MssInv.
* Script A = SolverMatrix
* Script P = Preconditioner
*
* Deflation methods considered
* -- Solve P A x = P b [ like Luscher ]
* DEF-1 M P A x = M P b [i.e. left precon]
* DEF-2 P^T M A x = P^T M b
* ADEF-1 Preconditioner = M P + Q [ Q + M + M A Q]
* ADEF-2 Preconditioner = P^T M + Q
* BNN Preconditioner = P^T M P + Q
* BNN2 Preconditioner = M P + P^TM +Q - M P A M
*
* Implement ADEF-2
*
* Vstart = P^Tx + Qb
* M1 = P^TM + Q
* M2=M3=1
* Vout = x
*/
// abstract base
template<class Field, class CoarseField>
class TwoLevelFlexiblePcg : public LinearFunction<Field>
{
public:
int verbose;
RealD Tolerance;
Integer MaxIterations;
const int mmax = 5;
GridBase *grid;
GridBase *coarsegrid;
LinearOperatorBase<Field> *_Linop
OperatorFunction<Field> *_Smoother,
LinearFunction<CoarseField> *_CoarseSolver;
// Need somthing that knows how to get from Coarse to fine and back again
// more most opertor functions
TwoLevelFlexiblePcg(RealD tol,
Integer maxit,
LinearOperatorBase<Field> *Linop,
LinearOperatorBase<Field> *SmootherLinop,
OperatorFunction<Field> *Smoother,
OperatorFunction<CoarseField> CoarseLinop
) :
Tolerance(tol),
MaxIterations(maxit),
_Linop(Linop),
_PreconditionerLinop(PrecLinop),
_Preconditioner(Preconditioner)
{
verbose=0;
};
// The Pcg routine is common to all, but the various matrices differ from derived
// implementation to derived implmentation
void operator() (const Field &src, Field &psi){
void operator() (const Field &src, Field &psi){
psi.Checkerboard() = src.Checkerboard();
grid = src.Grid();
RealD f;
RealD rtzp,rtz,a,d,b;
RealD rptzp;
RealD tn;
RealD guess = norm2(psi);
RealD ssq = norm2(src);
RealD rsq = ssq*Tolerance*Tolerance;
/////////////////////////////
// Set up history vectors
/////////////////////////////
std::vector<Field> p (mmax,grid);
std::vector<Field> mmp(mmax,grid);
std::vector<RealD> pAp(mmax);
Field x (grid); x = psi;
Field z (grid);
Field tmp(grid);
Field r (grid);
Field mu (grid);
//////////////////////////
// x0 = Vstart -- possibly modify guess
//////////////////////////
x=src;
Vstart(x,src);
// r0 = b -A x0
HermOp(x,mmp); // Shouldn't this be something else?
axpy (r, -1.0,mmp[0], src); // Recomputes r=src-Ax0
//////////////////////////////////
// Compute z = M1 x
//////////////////////////////////
M1(r,z,tmp,mp,SmootherMirs);
rtzp =real(innerProduct(r,z));
///////////////////////////////////////
// Solve for Mss mu = P A z and set p = z-mu
// Def2: p = 1 - Q Az = Pright z
// Other algos M2 is trivial
///////////////////////////////////////
M2(z,p[0]);
for (int k=0;k<=MaxIterations;k++){
int peri_k = k % mmax;
int peri_kp = (k+1) % mmax;
rtz=rtzp;
d= M3(p[peri_k],mp,mmp[peri_k],tmp);
a = rtz/d;
// Memorise this
pAp[peri_k] = d;
axpy(x,a,p[peri_k],x);
RealD rn = axpy_norm(r,-a,mmp[peri_k],r);
// Compute z = M x
M1(r,z,tmp,mp);
rtzp =real(innerProduct(r,z));
M2(z,mu); // ADEF-2 this is identity. Axpy possible to eliminate
p[peri_kp]=p[peri_k];
// Standard search direction p -> z + b p ; b =
b = (rtzp)/rtz;
int northog;
// northog = (peri_kp==0)?1:peri_kp; // This is the fCG(mmax) algorithm
northog = (k>mmax-1)?(mmax-1):k; // This is the fCG-Tr(mmax-1) algorithm
for(int back=0; back < northog; back++){
int peri_back = (k-back)%mmax;
RealD pbApk= real(innerProduct(mmp[peri_back],p[peri_kp]));
RealD beta = -pbApk/pAp[peri_back];
axpy(p[peri_kp],beta,p[peri_back],p[peri_kp]);
}
RealD rrn=sqrt(rn/ssq);
std::cout<<GridLogMessage<<"TwoLevelfPcg: k= "<<k<<" residual = "<<rrn<<std::endl;
// Stopping condition
if ( rn <= rsq ) {
HermOp(x,mmp); // Shouldn't this be something else?
axpy(tmp,-1.0,src,mmp[0]);
RealD psinorm = sqrt(norm2(x));
RealD srcnorm = sqrt(norm2(src));
RealD tmpnorm = sqrt(norm2(tmp));
RealD true_residual = tmpnorm/srcnorm;
std::cout<<GridLogMessage<<"TwoLevelfPcg: true residual is "<<true_residual<<std::endl;
std::cout<<GridLogMessage<<"TwoLevelfPcg: target residual was"<<Tolerance<<std::endl;
return k;
}
}
// Non-convergence
assert(0);
}
public:
virtual void M(Field & in,Field & out,Field & tmp) {
}
virtual void M1(Field & in, Field & out) {// the smoother
// [PTM+Q] in = [1 - Q A] M in + Q in = Min + Q [ in -A Min]
Field tmp(grid);
Field Min(grid);
PcgM(in,Min); // Smoother call
HermOp(Min,out);
axpy(tmp,-1.0,out,in); // tmp = in - A Min
ProjectToSubspace(tmp,PleftProj);
ApplyInverse(PleftProj,PleftMss_proj); // Ass^{-1} [in - A Min]_s
PromoteFromSubspace(PleftMss_proj,tmp);// tmp = Q[in - A Min]
axpy(out,1.0,Min,tmp); // Min+tmp
}
virtual void M2(const Field & in, Field & out) {
out=in;
// Must override for Def2 only
// case PcgDef2:
// Pright(in,out);
// break;
}
virtual RealD M3(const Field & p, Field & mmp){
double d,dd;
HermOpAndNorm(p,mmp,d,dd);
return dd;
// Must override for Def1 only
// case PcgDef1:
// d=linop_d->Mprec(p,mmp,tmp,0,1);// Dag no
// linop_d->Mprec(mmp,mp,tmp,1);// Dag yes
// Pleft(mp,mmp);
// d=real(linop_d->inner(p,mmp));
}
virtual void VstartDef2(Field & xconst Field & src){
//case PcgDef2:
//case PcgAdef2:
//case PcgAdef2f:
//case PcgV11f:
///////////////////////////////////
// Choose x_0 such that
// x_0 = guess + (A_ss^inv) r_s = guess + Ass_inv [src -Aguess]
// = [1 - Ass_inv A] Guess + Assinv src
// = P^T guess + Assinv src
// = Vstart [Tang notation]
// This gives:
// W^T (src - A x_0) = src_s - A guess_s - r_s
// = src_s - (A guess)_s - src_s + (A guess)_s
// = 0
///////////////////////////////////
Field r(grid);
Field mmp(grid);
HermOp(x,mmp);
axpy (r, -1.0, mmp, src); // r_{-1} = src - A x
ProjectToSubspace(r,PleftProj);
ApplyInverseCG(PleftProj,PleftMss_proj); // Ass^{-1} r_s
PromoteFromSubspace(PleftMss_proj,mmp);
x=x+mmp;
}
virtual void Vstart(Field & x,const Field & src){
return;
}
/////////////////////////////////////////////////////////////////////
// Only Def1 has non-trivial Vout. Override in Def1
/////////////////////////////////////////////////////////////////////
virtual void Vout (Field & in, Field & out,Field & src){
out = in;
//case PcgDef1:
// //Qb + PT x
// ProjectToSubspace(src,PleftProj);
// ApplyInverse(PleftProj,PleftMss_proj); // Ass^{-1} r_s
// PromoteFromSubspace(PleftMss_proj,tmp);
//
// Pright(in,out);
//
// linop_d->axpy(out,tmp,out,1.0);
// break;
}
////////////////////////////////////////////////////////////////////////////////////////////////
// Pright and Pleft are common to all implementations
////////////////////////////////////////////////////////////////////////////////////////////////
virtual void Pright(Field & in,Field & out){
// P_R = [ 1 0 ]
// [ -Mss^-1 Msb 0 ]
Field in_sbar(grid);
ProjectToSubspace(in,PleftProj);
PromoteFromSubspace(PleftProj,out);
axpy(in_sbar,-1.0,out,in); // in_sbar = in - in_s
HermOp(in_sbar,out);
ProjectToSubspace(out,PleftProj); // Mssbar in_sbar (project)
ApplyInverse (PleftProj,PleftMss_proj); // Mss^{-1} Mssbar
PromoteFromSubspace(PleftMss_proj,out); //
axpy(out,-1.0,out,in_sbar); // in_sbar - Mss^{-1} Mssbar in_sbar
}
virtual void Pleft (Field & in,Field & out){
// P_L = [ 1 -Mbs Mss^-1]
// [ 0 0 ]
Field in_sbar(grid);
Field tmp2(grid);
Field Mtmp(grid);
ProjectToSubspace(in,PleftProj);
PromoteFromSubspace(PleftProj,out);
axpy(in_sbar,-1.0,out,in); // in_sbar = in - in_s
ApplyInverse(PleftProj,PleftMss_proj); // Mss^{-1} in_s
PromoteFromSubspace(PleftMss_proj,out);
HermOp(out,Mtmp);
ProjectToSubspace(Mtmp,PleftProj); // Msbar s Mss^{-1}
PromoteFromSubspace(PleftProj,tmp2);
axpy(out,-1.0,tmp2,Mtmp);
axpy(out,-1.0,out,in_sbar); // in_sbar - Msbars Mss^{-1} in_s
}
}
template<class Field>
class TwoLevelFlexiblePcgADef2 : public TwoLevelFlexiblePcg<Field> {
public:
virtual void M(Field & in,Field & out,Field & tmp){
}
virtual void M1(Field & in, Field & out,Field & tmp,Field & mp){
}
virtual void M2(Field & in, Field & out){
}
virtual RealD M3(Field & p, Field & mp,Field & mmp, Field & tmp){
}
virtual void Vstart(Field & in, Field & src, Field & r, Field & mp, Field & mmp, Field & tmp){
}
}
/*
template<class Field>
class TwoLevelFlexiblePcgAD : public TwoLevelFlexiblePcg<Field> {
public:
virtual void M(Field & in,Field & out,Field & tmp);
virtual void M1(Field & in, Field & out,Field & tmp,Field & mp);
virtual void M2(Field & in, Field & out);
virtual RealD M3(Field & p, Field & mp,Field & mmp, Field & tmp);
virtual void Vstart(Field & in, Field & src, Field & r, Field & mp, Field & mmp, Field & tmp);
}
template<class Field>
class TwoLevelFlexiblePcgDef1 : public TwoLevelFlexiblePcg<Field> {
public:
virtual void M(Field & in,Field & out,Field & tmp);
virtual void M1(Field & in, Field & out,Field & tmp,Field & mp);
virtual void M2(Field & in, Field & out);
virtual RealD M3(Field & p, Field & mp,Field & mmp, Field & tmp);
virtual void Vstart(Field & in, Field & src, Field & r, Field & mp, Field & mmp, Field & tmp);
virtual void Vout (Field & in, Field & out,Field & src,Field & tmp);
}
template<class Field>
class TwoLevelFlexiblePcgDef2 : public TwoLevelFlexiblePcg<Field> {
public:
virtual void M(Field & in,Field & out,Field & tmp);
virtual void M1(Field & in, Field & out,Field & tmp,Field & mp);
virtual void M2(Field & in, Field & out);
virtual RealD M3(Field & p, Field & mp,Field & mmp, Field & tmp);
virtual void Vstart(Field & in, Field & src, Field & r, Field & mp, Field & mmp, Field & tmp);
}
template<class Field>
class TwoLevelFlexiblePcgV11: public TwoLevelFlexiblePcg<Field> {
public:
virtual void M(Field & in,Field & out,Field & tmp);
virtual void M1(Field & in, Field & out,Field & tmp,Field & mp);
virtual void M2(Field & in, Field & out);
virtual RealD M3(Field & p, Field & mp,Field & mmp, Field & tmp);
virtual void Vstart(Field & in, Field & src, Field & r, Field & mp, Field & mmp, Field & tmp);
}
*/
#endif

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@ -0,0 +1,694 @@
/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/BlockConjugateGradient.h
Copyright (C) 2017
Author: Azusa Yamaguchi <ayamaguc@staffmail.ed.ac.uk>
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution
directory
*************************************************************************************/
/* END LEGAL */
#pragma once
NAMESPACE_BEGIN(Grid);
enum BlockCGtype { BlockCG, BlockCGrQ, CGmultiRHS, BlockCGVec, BlockCGrQVec };
//////////////////////////////////////////////////////////////////////////
// Block conjugate gradient. Dimension zero should be the block direction
//////////////////////////////////////////////////////////////////////////
template <class Field>
class BlockConjugateGradient : public OperatorFunction<Field> {
public:
typedef typename Field::scalar_type scomplex;
int blockDim ;
int Nblock;
BlockCGtype CGtype;
bool ErrorOnNoConverge; // throw an assert when the CG fails to converge.
// Defaults true.
RealD Tolerance;
Integer MaxIterations;
Integer IterationsToComplete; //Number of iterations the CG took to finish. Filled in upon completion
Integer PrintInterval; //GridLogMessages or Iterative
BlockConjugateGradient(BlockCGtype cgtype,int _Orthog,RealD tol, Integer maxit, bool err_on_no_conv = true)
: Tolerance(tol), CGtype(cgtype), blockDim(_Orthog), MaxIterations(maxit), ErrorOnNoConverge(err_on_no_conv),PrintInterval(100)
{};
////////////////////////////////////////////////////////////////////////////////////////////////////
// Thin QR factorisation (google it)
////////////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////
//Dimensions
// R_{ferm x Nblock} = Q_{ferm x Nblock} x C_{Nblock x Nblock} -> ferm x Nblock
//
// Rdag R = m_rr = Herm = L L^dag <-- Cholesky decomposition (LLT routine in Eigen)
//
// Q C = R => Q = R C^{-1}
//
// Want Ident = Q^dag Q = C^{-dag} R^dag R C^{-1} = C^{-dag} L L^dag C^{-1} = 1_{Nblock x Nblock}
//
// Set C = L^{dag}, and then Q^dag Q = ident
//
// Checks:
// Cdag C = Rdag R ; passes.
// QdagQ = 1 ; passes
////////////////////////////////////////////////////////////////////////////////////////////////////
void ThinQRfact (Eigen::MatrixXcd &m_rr,
Eigen::MatrixXcd &C,
Eigen::MatrixXcd &Cinv,
Field & Q,
const Field & R)
{
int Orthog = blockDim; // First dimension is block dim; this is an assumption
sliceInnerProductMatrix(m_rr,R,R,Orthog);
// Force manifest hermitian to avoid rounding related
m_rr = 0.5*(m_rr+m_rr.adjoint());
Eigen::MatrixXcd L = m_rr.llt().matrixL();
C = L.adjoint();
Cinv = C.inverse();
////////////////////////////////////////////////////////////////////////////////////////////////////
// Q = R C^{-1}
//
// Q_j = R_i Cinv(i,j)
//
// NB maddMatrix conventions are Right multiplication X[j] a[j,i] already
////////////////////////////////////////////////////////////////////////////////////////////////////
sliceMulMatrix(Q,Cinv,R,Orthog);
}
// see comments above
void ThinQRfact (Eigen::MatrixXcd &m_rr,
Eigen::MatrixXcd &C,
Eigen::MatrixXcd &Cinv,
std::vector<Field> & Q,
const std::vector<Field> & R)
{
InnerProductMatrix(m_rr,R,R);
m_rr = 0.5*(m_rr+m_rr.adjoint());
Eigen::MatrixXcd L = m_rr.llt().matrixL();
C = L.adjoint();
Cinv = C.inverse();
MulMatrix(Q,Cinv,R);
}
////////////////////////////////////////////////////////////////////////////////////////////////////
// Call one of several implementations
////////////////////////////////////////////////////////////////////////////////////////////////////
void operator()(LinearOperatorBase<Field> &Linop, const Field &Src, Field &Psi)
{
if ( CGtype == BlockCGrQ ) {
BlockCGrQsolve(Linop,Src,Psi);
} else if (CGtype == CGmultiRHS ) {
CGmultiRHSsolve(Linop,Src,Psi);
} else {
assert(0);
}
}
virtual void operator()(LinearOperatorBase<Field> &Linop, const std::vector<Field> &Src, std::vector<Field> &Psi)
{
if ( CGtype == BlockCGrQVec ) {
BlockCGrQsolveVec(Linop,Src,Psi);
} else {
assert(0);
}
}
////////////////////////////////////////////////////////////////////////////
// BlockCGrQ implementation:
//--------------------------
// X is guess/Solution
// B is RHS
// Solve A X_i = B_i ; i refers to Nblock index
////////////////////////////////////////////////////////////////////////////
void BlockCGrQsolve(LinearOperatorBase<Field> &Linop, const Field &B, Field &X)
{
int Orthog = blockDim; // First dimension is block dim; this is an assumption
Nblock = B.Grid()->_fdimensions[Orthog];
/* FAKE */
Nblock=8;
std::cout<<GridLogMessage<<" Block Conjugate Gradient : Orthog "<<Orthog<<" Nblock "<<Nblock<<std::endl;
X.checkerboard = B.checkerboard;
conformable(X, B);
Field tmp(B);
Field Q(B);
Field D(B);
Field Z(B);
Field AD(B);
Eigen::MatrixXcd m_DZ = Eigen::MatrixXcd::Identity(Nblock,Nblock);
Eigen::MatrixXcd m_M = Eigen::MatrixXcd::Identity(Nblock,Nblock);
Eigen::MatrixXcd m_rr = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_C = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_Cinv = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_S = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_Sinv = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_tmp = Eigen::MatrixXcd::Identity(Nblock,Nblock);
Eigen::MatrixXcd m_tmp1 = Eigen::MatrixXcd::Identity(Nblock,Nblock);
// Initial residual computation & set up
std::vector<RealD> residuals(Nblock);
std::vector<RealD> ssq(Nblock);
sliceNorm(ssq,B,Orthog);
RealD sssum=0;
for(int b=0;b<Nblock;b++) sssum+=ssq[b];
sliceNorm(residuals,B,Orthog);
for(int b=0;b<Nblock;b++){ assert(std::isnan(residuals[b])==0); }
sliceNorm(residuals,X,Orthog);
for(int b=0;b<Nblock;b++){ assert(std::isnan(residuals[b])==0); }
/************************************************************************
* Block conjugate gradient rQ (Sebastien Birk Thesis, after Dubrulle 2001)
************************************************************************
* Dimensions:
*
* X,B==(Nferm x Nblock)
* A==(Nferm x Nferm)
*
* Nferm = Nspin x Ncolour x Ncomplex x Nlattice_site
*
* QC = R = B-AX, D = Q ; QC => Thin QR factorisation (google it)
* for k:
* Z = AD
* M = [D^dag Z]^{-1}
* X = X + D MC
* QS = Q - ZM
* D = Q + D S^dag
* C = S C
*/
///////////////////////////////////////
// Initial block: initial search dir is guess
///////////////////////////////////////
std::cout << GridLogMessage<<"BlockCGrQ algorithm initialisation " <<std::endl;
//1. QC = R = B-AX, D = Q ; QC => Thin QR factorisation (google it)
Linop.HermOp(X, AD);
tmp = B - AD;
ThinQRfact (m_rr, m_C, m_Cinv, Q, tmp);
D=Q;
std::cout << GridLogMessage<<"BlockCGrQ computed initial residual and QR fact " <<std::endl;
///////////////////////////////////////
// Timers
///////////////////////////////////////
GridStopWatch sliceInnerTimer;
GridStopWatch sliceMaddTimer;
GridStopWatch QRTimer;
GridStopWatch MatrixTimer;
GridStopWatch SolverTimer;
SolverTimer.Start();
int k;
for (k = 1; k <= MaxIterations; k++) {
//3. Z = AD
MatrixTimer.Start();
Linop.HermOp(D, Z);
MatrixTimer.Stop();
//4. M = [D^dag Z]^{-1}
sliceInnerTimer.Start();
sliceInnerProductMatrix(m_DZ,D,Z,Orthog);
sliceInnerTimer.Stop();
m_M = m_DZ.inverse();
//5. X = X + D MC
m_tmp = m_M * m_C;
sliceMaddTimer.Start();
sliceMaddMatrix(X,m_tmp, D,X,Orthog);
sliceMaddTimer.Stop();
//6. QS = Q - ZM
sliceMaddTimer.Start();
sliceMaddMatrix(tmp,m_M,Z,Q,Orthog,-1.0);
sliceMaddTimer.Stop();
QRTimer.Start();
ThinQRfact (m_rr, m_S, m_Sinv, Q, tmp);
QRTimer.Stop();
//7. D = Q + D S^dag
m_tmp = m_S.adjoint();
sliceMaddTimer.Start();
sliceMaddMatrix(D,m_tmp,D,Q,Orthog);
sliceMaddTimer.Stop();
//8. C = S C
m_C = m_S*m_C;
/*********************
* convergence monitor
*********************
*/
m_rr = m_C.adjoint() * m_C;
RealD max_resid=0;
RealD rrsum=0;
RealD rr;
for(int b=0;b<Nblock;b++) {
rrsum+=real(m_rr(b,b));
rr = real(m_rr(b,b))/ssq[b];
if ( rr > max_resid ) max_resid = rr;
}
std::cout << GridLogIterative << "\titeration "<<k<<" rr_sum "<<rrsum<<" ssq_sum "<< sssum
<<" ave "<<std::sqrt(rrsum/sssum) << " max "<< max_resid <<std::endl;
if ( max_resid < Tolerance*Tolerance ) {
SolverTimer.Stop();
std::cout << GridLogMessage<<"BlockCGrQ converged in "<<k<<" iterations"<<std::endl;
for(int b=0;b<Nblock;b++){
std::cout << GridLogMessage<< "\t\tblock "<<b<<" computed resid "
<< std::sqrt(real(m_rr(b,b))/ssq[b])<<std::endl;
}
std::cout << GridLogMessage<<"\tMax residual is "<<std::sqrt(max_resid)<<std::endl;
Linop.HermOp(X, AD);
AD = AD-B;
std::cout << GridLogMessage <<"\t True residual is " << std::sqrt(norm2(AD)/norm2(B)) <<std::endl;
std::cout << GridLogMessage << "Time Breakdown "<<std::endl;
std::cout << GridLogMessage << "\tElapsed " << SolverTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tMatrix " << MatrixTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tInnerProd " << sliceInnerTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tMaddMatrix " << sliceMaddTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tThinQRfact " << QRTimer.Elapsed() <<std::endl;
IterationsToComplete = k;
return;
}
}
std::cout << GridLogMessage << "BlockConjugateGradient(rQ) did NOT converge" << std::endl;
if (ErrorOnNoConverge) assert(0);
IterationsToComplete = k;
}
//////////////////////////////////////////////////////////////////////////
// multiRHS conjugate gradient. Dimension zero should be the block direction
// Use this for spread out across nodes
//////////////////////////////////////////////////////////////////////////
void CGmultiRHSsolve(LinearOperatorBase<Field> &Linop, const Field &Src, Field &Psi)
{
int Orthog = blockDim; // First dimension is block dim
Nblock = Src.Grid()->_fdimensions[Orthog];
std::cout<<GridLogMessage<<"MultiRHS Conjugate Gradient : Orthog "<<Orthog<<" Nblock "<<Nblock<<std::endl;
Psi.checkerboard = Src.checkerboard;
conformable(Psi, Src);
Field P(Src);
Field AP(Src);
Field R(Src);
std::vector<ComplexD> v_pAp(Nblock);
std::vector<RealD> v_rr (Nblock);
std::vector<RealD> v_rr_inv(Nblock);
std::vector<RealD> v_alpha(Nblock);
std::vector<RealD> v_beta(Nblock);
// Initial residual computation & set up
std::vector<RealD> residuals(Nblock);
std::vector<RealD> ssq(Nblock);
sliceNorm(ssq,Src,Orthog);
RealD sssum=0;
for(int b=0;b<Nblock;b++) sssum+=ssq[b];
sliceNorm(residuals,Src,Orthog);
for(int b=0;b<Nblock;b++){ assert(std::isnan(residuals[b])==0); }
sliceNorm(residuals,Psi,Orthog);
for(int b=0;b<Nblock;b++){ assert(std::isnan(residuals[b])==0); }
// Initial search dir is guess
Linop.HermOp(Psi, AP);
R = Src - AP;
P = R;
sliceNorm(v_rr,R,Orthog);
GridStopWatch sliceInnerTimer;
GridStopWatch sliceMaddTimer;
GridStopWatch sliceNormTimer;
GridStopWatch MatrixTimer;
GridStopWatch SolverTimer;
SolverTimer.Start();
int k;
for (k = 1; k <= MaxIterations; k++) {
RealD rrsum=0;
for(int b=0;b<Nblock;b++) rrsum+=real(v_rr[b]);
std::cout << GridLogIterative << "\titeration "<<k<<" rr_sum "<<rrsum<<" ssq_sum "<< sssum
<<" / "<<std::sqrt(rrsum/sssum) <<std::endl;
MatrixTimer.Start();
Linop.HermOp(P, AP);
MatrixTimer.Stop();
// Alpha
sliceInnerTimer.Start();
sliceInnerProductVector(v_pAp,P,AP,Orthog);
sliceInnerTimer.Stop();
for(int b=0;b<Nblock;b++){
v_alpha[b] = v_rr[b]/real(v_pAp[b]);
}
// Psi, R update
sliceMaddTimer.Start();
sliceMaddVector(Psi,v_alpha, P,Psi,Orthog); // add alpha * P to psi
sliceMaddVector(R ,v_alpha,AP, R,Orthog,-1.0);// sub alpha * AP to resid
sliceMaddTimer.Stop();
// Beta
for(int b=0;b<Nblock;b++){
v_rr_inv[b] = 1.0/v_rr[b];
}
sliceNormTimer.Start();
sliceNorm(v_rr,R,Orthog);
sliceNormTimer.Stop();
for(int b=0;b<Nblock;b++){
v_beta[b] = v_rr_inv[b] *v_rr[b];
}
// Search update
sliceMaddTimer.Start();
sliceMaddVector(P,v_beta,P,R,Orthog);
sliceMaddTimer.Stop();
/*********************
* convergence monitor
*********************
*/
RealD max_resid=0;
for(int b=0;b<Nblock;b++){
RealD rr = v_rr[b]/ssq[b];
if ( rr > max_resid ) max_resid = rr;
}
if ( max_resid < Tolerance*Tolerance ) {
SolverTimer.Stop();
std::cout << GridLogMessage<<"MultiRHS solver converged in " <<k<<" iterations"<<std::endl;
for(int b=0;b<Nblock;b++){
std::cout << GridLogMessage<< "\t\tBlock "<<b<<" computed resid "<< std::sqrt(v_rr[b]/ssq[b])<<std::endl;
}
std::cout << GridLogMessage<<"\tMax residual is "<<std::sqrt(max_resid)<<std::endl;
Linop.HermOp(Psi, AP);
AP = AP-Src;
std::cout <<GridLogMessage << "\tTrue residual is " << std::sqrt(norm2(AP)/norm2(Src)) <<std::endl;
std::cout << GridLogMessage << "Time Breakdown "<<std::endl;
std::cout << GridLogMessage << "\tElapsed " << SolverTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tMatrix " << MatrixTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tInnerProd " << sliceInnerTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tNorm " << sliceNormTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tMaddMatrix " << sliceMaddTimer.Elapsed() <<std::endl;
IterationsToComplete = k;
return;
}
}
std::cout << GridLogMessage << "MultiRHSConjugateGradient did NOT converge" << std::endl;
if (ErrorOnNoConverge) assert(0);
IterationsToComplete = k;
}
void InnerProductMatrix(Eigen::MatrixXcd &m , const std::vector<Field> &X, const std::vector<Field> &Y){
for(int b=0;b<Nblock;b++){
for(int bp=0;bp<Nblock;bp++) {
m(b,bp) = innerProduct(X[b],Y[bp]);
}}
}
void MaddMatrix(std::vector<Field> &AP, Eigen::MatrixXcd &m , const std::vector<Field> &X,const std::vector<Field> &Y,RealD scale=1.0){
// Should make this cache friendly with site outermost, parallel_for
// Deal with case AP aliases with either Y or X
std::vector<Field> tmp(Nblock,X[0]);
for(int b=0;b<Nblock;b++){
tmp[b] = Y[b];
for(int bp=0;bp<Nblock;bp++) {
tmp[b] = tmp[b] + (scale*m(bp,b))*X[bp];
}
}
for(int b=0;b<Nblock;b++){
AP[b] = tmp[b];
}
}
void MulMatrix(std::vector<Field> &AP, Eigen::MatrixXcd &m , const std::vector<Field> &X){
// Should make this cache friendly with site outermost, parallel_for
for(int b=0;b<Nblock;b++){
AP[b] = Zero();
for(int bp=0;bp<Nblock;bp++) {
AP[b] += (m(bp,b))*X[bp];
}
}
}
double normv(const std::vector<Field> &P){
double nn = 0.0;
for(int b=0;b<Nblock;b++) {
nn+=norm2(P[b]);
}
return nn;
}
////////////////////////////////////////////////////////////////////////////
// BlockCGrQvec implementation:
//--------------------------
// X is guess/Solution
// B is RHS
// Solve A X_i = B_i ; i refers to Nblock index
////////////////////////////////////////////////////////////////////////////
void BlockCGrQsolveVec(LinearOperatorBase<Field> &Linop, const std::vector<Field> &B, std::vector<Field> &X)
{
Nblock = B.size();
assert(Nblock == X.size());
std::cout<<GridLogMessage<<" Block Conjugate Gradient Vec rQ : Nblock "<<Nblock<<std::endl;
for(int b=0;b<Nblock;b++){
X[b].checkerboard = B[b].checkerboard;
conformable(X[b], B[b]);
conformable(X[b], X[0]);
}
Field Fake(B[0]);
std::vector<Field> tmp(Nblock,Fake);
std::vector<Field> Q(Nblock,Fake);
std::vector<Field> D(Nblock,Fake);
std::vector<Field> Z(Nblock,Fake);
std::vector<Field> AD(Nblock,Fake);
Eigen::MatrixXcd m_DZ = Eigen::MatrixXcd::Identity(Nblock,Nblock);
Eigen::MatrixXcd m_M = Eigen::MatrixXcd::Identity(Nblock,Nblock);
Eigen::MatrixXcd m_rr = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_C = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_Cinv = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_S = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_Sinv = Eigen::MatrixXcd::Zero(Nblock,Nblock);
Eigen::MatrixXcd m_tmp = Eigen::MatrixXcd::Identity(Nblock,Nblock);
Eigen::MatrixXcd m_tmp1 = Eigen::MatrixXcd::Identity(Nblock,Nblock);
// Initial residual computation & set up
std::vector<RealD> residuals(Nblock);
std::vector<RealD> ssq(Nblock);
RealD sssum=0;
for(int b=0;b<Nblock;b++){ ssq[b] = norm2(B[b]);}
for(int b=0;b<Nblock;b++) sssum+=ssq[b];
for(int b=0;b<Nblock;b++){ residuals[b] = norm2(B[b]);}
for(int b=0;b<Nblock;b++){ assert(std::isnan(residuals[b])==0); }
for(int b=0;b<Nblock;b++){ residuals[b] = norm2(X[b]);}
for(int b=0;b<Nblock;b++){ assert(std::isnan(residuals[b])==0); }
/************************************************************************
* Block conjugate gradient rQ (Sebastien Birk Thesis, after Dubrulle 2001)
************************************************************************
* Dimensions:
*
* X,B==(Nferm x Nblock)
* A==(Nferm x Nferm)
*
* Nferm = Nspin x Ncolour x Ncomplex x Nlattice_site
*
* QC = R = B-AX, D = Q ; QC => Thin QR factorisation (google it)
* for k:
* Z = AD
* M = [D^dag Z]^{-1}
* X = X + D MC
* QS = Q - ZM
* D = Q + D S^dag
* C = S C
*/
///////////////////////////////////////
// Initial block: initial search dir is guess
///////////////////////////////////////
std::cout << GridLogMessage<<"BlockCGrQvec algorithm initialisation " <<std::endl;
//1. QC = R = B-AX, D = Q ; QC => Thin QR factorisation (google it)
for(int b=0;b<Nblock;b++) {
Linop.HermOp(X[b], AD[b]);
tmp[b] = B[b] - AD[b];
}
ThinQRfact (m_rr, m_C, m_Cinv, Q, tmp);
for(int b=0;b<Nblock;b++) D[b]=Q[b];
std::cout << GridLogMessage<<"BlockCGrQ vec computed initial residual and QR fact " <<std::endl;
///////////////////////////////////////
// Timers
///////////////////////////////////////
GridStopWatch sliceInnerTimer;
GridStopWatch sliceMaddTimer;
GridStopWatch QRTimer;
GridStopWatch MatrixTimer;
GridStopWatch SolverTimer;
SolverTimer.Start();
int k;
for (k = 1; k <= MaxIterations; k++) {
//3. Z = AD
MatrixTimer.Start();
for(int b=0;b<Nblock;b++) Linop.HermOp(D[b], Z[b]);
MatrixTimer.Stop();
//4. M = [D^dag Z]^{-1}
sliceInnerTimer.Start();
InnerProductMatrix(m_DZ,D,Z);
sliceInnerTimer.Stop();
m_M = m_DZ.inverse();
//5. X = X + D MC
m_tmp = m_M * m_C;
sliceMaddTimer.Start();
MaddMatrix(X,m_tmp, D,X);
sliceMaddTimer.Stop();
//6. QS = Q - ZM
sliceMaddTimer.Start();
MaddMatrix(tmp,m_M,Z,Q,-1.0);
sliceMaddTimer.Stop();
QRTimer.Start();
ThinQRfact (m_rr, m_S, m_Sinv, Q, tmp);
QRTimer.Stop();
//7. D = Q + D S^dag
m_tmp = m_S.adjoint();
sliceMaddTimer.Start();
MaddMatrix(D,m_tmp,D,Q);
sliceMaddTimer.Stop();
//8. C = S C
m_C = m_S*m_C;
/*********************
* convergence monitor
*********************
*/
m_rr = m_C.adjoint() * m_C;
RealD max_resid=0;
RealD rrsum=0;
RealD rr;
for(int b=0;b<Nblock;b++) {
rrsum+=real(m_rr(b,b));
rr = real(m_rr(b,b))/ssq[b];
if ( rr > max_resid ) max_resid = rr;
}
std::cout << GridLogIterative << "\t Block Iteration "<<k<<" ave resid "<< sqrt(rrsum/sssum) << " max "<< sqrt(max_resid) <<std::endl;
if ( max_resid < Tolerance*Tolerance ) {
SolverTimer.Stop();
std::cout << GridLogMessage<<"BlockCGrQ converged in "<<k<<" iterations"<<std::endl;
for(int b=0;b<Nblock;b++){
std::cout << GridLogMessage<< "\t\tblock "<<b<<" computed resid "<< std::sqrt(real(m_rr(b,b))/ssq[b])<<std::endl;
}
std::cout << GridLogMessage<<"\tMax residual is "<<std::sqrt(max_resid)<<std::endl;
for(int b=0;b<Nblock;b++) Linop.HermOp(X[b], AD[b]);
for(int b=0;b<Nblock;b++) AD[b] = AD[b]-B[b];
std::cout << GridLogMessage <<"\t True residual is " << std::sqrt(normv(AD)/normv(B)) <<std::endl;
std::cout << GridLogMessage << "Time Breakdown "<<std::endl;
std::cout << GridLogMessage << "\tElapsed " << SolverTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tMatrix " << MatrixTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tInnerProd " << sliceInnerTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tMaddMatrix " << sliceMaddTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tThinQRfact " << QRTimer.Elapsed() <<std::endl;
IterationsToComplete = k;
return;
}
}
std::cout << GridLogMessage << "BlockConjugateGradient(rQ) did NOT converge" << std::endl;
if (ErrorOnNoConverge) assert(0);
IterationsToComplete = k;
}
};
NAMESPACE_END(Grid);

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/ConjugateGradient.h
Copyright (C) 2015
Author: Azusa Yamaguchi <ayamaguc@staffmail.ed.ac.uk>
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
Author: paboyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution
directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_CONJUGATE_GRADIENT_H
#define GRID_CONJUGATE_GRADIENT_H
NAMESPACE_BEGIN(Grid);
/////////////////////////////////////////////////////////////
// Base classes for iterative processes based on operators
// single input vec, single output vec.
/////////////////////////////////////////////////////////////
template <class Field>
class ConjugateGradient : public OperatorFunction<Field> {
public:
bool ErrorOnNoConverge; // throw an assert when the CG fails to converge.
// Defaults true.
RealD Tolerance;
Integer MaxIterations;
Integer IterationsToComplete; //Number of iterations the CG took to finish. Filled in upon completion
ConjugateGradient(RealD tol, Integer maxit, bool err_on_no_conv = true)
: Tolerance(tol),
MaxIterations(maxit),
ErrorOnNoConverge(err_on_no_conv){};
void operator()(LinearOperatorBase<Field> &Linop, const Field &src, Field &psi) {
psi.Checkerboard() = src.Checkerboard();
conformable(psi, src);
RealD cp, c, a, d, b, ssq, qq, b_pred;
Field p(src);
Field mmp(src);
Field r(src);
// Initial residual computation & set up
RealD guess = norm2(psi);
assert(std::isnan(guess) == 0);
Linop.HermOpAndNorm(psi, mmp, d, b);
r = src - mmp;
p = r;
a = norm2(p);
cp = a;
ssq = norm2(src);
std::cout << GridLogIterative << std::setprecision(8) << "ConjugateGradient: guess " << guess << std::endl;
std::cout << GridLogIterative << std::setprecision(8) << "ConjugateGradient: src " << ssq << std::endl;
std::cout << GridLogIterative << std::setprecision(8) << "ConjugateGradient: mp " << d << std::endl;
std::cout << GridLogIterative << std::setprecision(8) << "ConjugateGradient: mmp " << b << std::endl;
std::cout << GridLogIterative << std::setprecision(8) << "ConjugateGradient: cp,r " << cp << std::endl;
std::cout << GridLogIterative << std::setprecision(8) << "ConjugateGradient: p " << a << std::endl;
RealD rsq = Tolerance * Tolerance * ssq;
// Check if guess is really REALLY good :)
if (cp <= rsq) {
return;
}
std::cout << GridLogIterative << std::setprecision(8)
<< "ConjugateGradient: k=0 residual " << cp << " target " << rsq << std::endl;
GridStopWatch LinalgTimer;
GridStopWatch InnerTimer;
GridStopWatch AxpyNormTimer;
GridStopWatch LinearCombTimer;
GridStopWatch MatrixTimer;
GridStopWatch SolverTimer;
SolverTimer.Start();
int k;
for (k = 1; k <= MaxIterations*1000; k++) {
c = cp;
MatrixTimer.Start();
Linop.HermOp(p, mmp);
MatrixTimer.Stop();
LinalgTimer.Start();
InnerTimer.Start();
ComplexD dc = innerProduct(p,mmp);
InnerTimer.Stop();
d = dc.real();
a = c / d;
AxpyNormTimer.Start();
cp = axpy_norm(r, -a, mmp, r);
AxpyNormTimer.Stop();
b = cp / c;
LinearCombTimer.Start();
auto psi_v = psi.View();
auto p_v = p.View();
auto r_v = r.View();
parallel_for(int ss=0;ss<src.Grid()->oSites();ss++){
vstream(psi_v[ss], a * p_v[ss] + psi_v[ss]);
vstream(p_v [ss], b * p_v[ss] + r_v[ss]);
}
LinearCombTimer.Stop();
LinalgTimer.Stop();
std::cout << GridLogIterative << "ConjugateGradient: Iteration " << k
<< " residual^2 " << sqrt(cp/ssq) << " target " << Tolerance << std::endl;
// Stopping condition
if (cp <= rsq) {
SolverTimer.Stop();
Linop.HermOpAndNorm(psi, mmp, d, qq);
p = mmp - src;
RealD srcnorm = std::sqrt(norm2(src));
RealD resnorm = std::sqrt(norm2(p));
RealD true_residual = resnorm / srcnorm;
std::cout << GridLogMessage << "ConjugateGradient Converged on iteration " << k << std::endl;
std::cout << GridLogMessage << "\tComputed residual " << std::sqrt(cp / ssq)<<std::endl;
std::cout << GridLogMessage << "\tTrue residual " << true_residual<<std::endl;
std::cout << GridLogMessage << "\tTarget " << Tolerance << std::endl;
std::cout << GridLogMessage << "Time breakdown "<<std::endl;
std::cout << GridLogMessage << "\tElapsed " << SolverTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tMatrix " << MatrixTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tLinalg " << LinalgTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tInner " << InnerTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tAxpyNorm " << AxpyNormTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tLinearComb " << LinearCombTimer.Elapsed() <<std::endl;
if (ErrorOnNoConverge) assert(true_residual / Tolerance < 10000.0);
IterationsToComplete = k;
return;
}
}
std::cout << GridLogMessage << "ConjugateGradient did NOT converge"
<< std::endl;
if (ErrorOnNoConverge) assert(0);
IterationsToComplete = k;
}
};
NAMESPACE_END(Grid);
#endif

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/ConjugateGradientMixedPrec.h
Copyright (C) 2015
Author: Christopher Kelly <ckelly@phys.columbia.edu>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_CONJUGATE_GRADIENT_MIXED_PREC_H
#define GRID_CONJUGATE_GRADIENT_MIXED_PREC_H
NAMESPACE_BEGIN(Grid);
//Mixed precision restarted defect correction CG
template<class FieldD,class FieldF,
typename std::enable_if< getPrecision<FieldD>::value == 2, int>::type = 0,
typename std::enable_if< getPrecision<FieldF>::value == 1, int>::type = 0>
class MixedPrecisionConjugateGradient : public LinearFunction<FieldD> {
public:
RealD Tolerance;
RealD InnerTolerance; //Initial tolerance for inner CG. Defaults to Tolerance but can be changed
Integer MaxInnerIterations;
Integer MaxOuterIterations;
GridBase* SinglePrecGrid; //Grid for single-precision fields
RealD OuterLoopNormMult; //Stop the outer loop and move to a final double prec solve when the residual is OuterLoopNormMult * Tolerance
LinearOperatorBase<FieldF> &Linop_f;
LinearOperatorBase<FieldD> &Linop_d;
Integer TotalInnerIterations; //Number of inner CG iterations
Integer TotalOuterIterations; //Number of restarts
Integer TotalFinalStepIterations; //Number of CG iterations in final patch-up step
//Option to speed up *inner single precision* solves using a LinearFunction that produces a guess
LinearFunction<FieldF> *guesser;
MixedPrecisionConjugateGradient(RealD tol, Integer maxinnerit, Integer maxouterit, GridBase* _sp_grid, LinearOperatorBase<FieldF> &_Linop_f, LinearOperatorBase<FieldD> &_Linop_d) :
Linop_f(_Linop_f), Linop_d(_Linop_d),
Tolerance(tol), InnerTolerance(tol), MaxInnerIterations(maxinnerit), MaxOuterIterations(maxouterit), SinglePrecGrid(_sp_grid),
OuterLoopNormMult(100.), guesser(NULL){ };
void useGuesser(LinearFunction<FieldF> &g){
guesser = &g;
}
void operator() (const FieldD &src_d_in, FieldD &sol_d){
TotalInnerIterations = 0;
GridStopWatch TotalTimer;
TotalTimer.Start();
int cb = src_d_in.Checkerboard();
sol_d.Checkerboard() = cb;
RealD src_norm = norm2(src_d_in);
RealD stop = src_norm * Tolerance*Tolerance;
GridBase* DoublePrecGrid = src_d_in.Grid();
FieldD tmp_d(DoublePrecGrid);
tmp_d.Checkerboard() = cb;
FieldD tmp2_d(DoublePrecGrid);
tmp2_d.Checkerboard() = cb;
FieldD src_d(DoublePrecGrid);
src_d = src_d_in; //source for next inner iteration, computed from residual during operation
RealD inner_tol = InnerTolerance;
FieldF src_f(SinglePrecGrid);
src_f.Checkerboard() = cb;
FieldF sol_f(SinglePrecGrid);
sol_f.Checkerboard() = cb;
ConjugateGradient<FieldF> CG_f(inner_tol, MaxInnerIterations);
CG_f.ErrorOnNoConverge = false;
GridStopWatch InnerCGtimer;
GridStopWatch PrecChangeTimer;
Integer &outer_iter = TotalOuterIterations; //so it will be equal to the final iteration count
for(outer_iter = 0; outer_iter < MaxOuterIterations; outer_iter++){
//Compute double precision rsd and also new RHS vector.
Linop_d.HermOp(sol_d, tmp_d);
RealD norm = axpy_norm(src_d, -1., tmp_d, src_d_in); //src_d is residual vector
std::cout<<GridLogMessage<<"MixedPrecisionConjugateGradient: Outer iteration " <<outer_iter<<" residual "<< norm<< " target "<< stop<<std::endl;
if(norm < OuterLoopNormMult * stop){
std::cout<<GridLogMessage<<"MixedPrecisionConjugateGradient: Outer iteration converged on iteration " <<outer_iter <<std::endl;
break;
}
while(norm * inner_tol * inner_tol < stop) inner_tol *= 2; // inner_tol = sqrt(stop/norm) ??
PrecChangeTimer.Start();
precisionChange(src_f, src_d);
PrecChangeTimer.Stop();
sol_f = Zero();
//Optionally improve inner solver guess (eg using known eigenvectors)
if(guesser != NULL)
(*guesser)(src_f, sol_f);
//Inner CG
CG_f.Tolerance = inner_tol;
InnerCGtimer.Start();
CG_f(Linop_f, src_f, sol_f);
InnerCGtimer.Stop();
TotalInnerIterations += CG_f.IterationsToComplete;
//Convert sol back to double and add to double prec solution
PrecChangeTimer.Start();
precisionChange(tmp_d, sol_f);
PrecChangeTimer.Stop();
axpy(sol_d, 1.0, tmp_d, sol_d);
}
//Final trial CG
std::cout<<GridLogMessage<<"MixedPrecisionConjugateGradient: Starting final patch-up double-precision solve"<<std::endl;
ConjugateGradient<FieldD> CG_d(Tolerance, MaxInnerIterations);
CG_d(Linop_d, src_d_in, sol_d);
TotalFinalStepIterations = CG_d.IterationsToComplete;
TotalTimer.Stop();
std::cout<<GridLogMessage<<"MixedPrecisionConjugateGradient: Inner CG iterations " << TotalInnerIterations << " Restarts " << TotalOuterIterations << " Final CG iterations " << TotalFinalStepIterations << std::endl;
std::cout<<GridLogMessage<<"MixedPrecisionConjugateGradient: Total time " << TotalTimer.Elapsed() << " Precision change " << PrecChangeTimer.Elapsed() << " Inner CG total " << InnerCGtimer.Elapsed() << std::endl;
}
};
NAMESPACE_END(Grid);
#endif

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/ConjugateGradientMultiShift.h
Copyright (C) 2015
Author: Azusa Yamaguchi <ayamaguc@staffmail.ed.ac.uk>
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_CONJUGATE_MULTI_SHIFT_GRADIENT_H
#define GRID_CONJUGATE_MULTI_SHIFT_GRADIENT_H
NAMESPACE_BEGIN(Grid);
/////////////////////////////////////////////////////////////
// Base classes for iterative processes based on operators
// single input vec, single output vec.
/////////////////////////////////////////////////////////////
template<class Field>
class ConjugateGradientMultiShift : public OperatorMultiFunction<Field>,
public OperatorFunction<Field>
{
public:
RealD Tolerance;
Integer MaxIterations;
Integer IterationsToComplete; //Number of iterations the CG took to finish. Filled in upon completion
int verbose;
MultiShiftFunction shifts;
ConjugateGradientMultiShift(Integer maxit,MultiShiftFunction &_shifts) :
MaxIterations(maxit),
shifts(_shifts)
{
verbose=1;
}
void operator() (LinearOperatorBase<Field> &Linop, const Field &src, Field &psi)
{
GridBase *grid = src.Grid();
int nshift = shifts.order;
std::vector<Field> results(nshift,grid);
(*this)(Linop,src,results,psi);
}
void operator() (LinearOperatorBase<Field> &Linop, const Field &src, std::vector<Field> &results, Field &psi)
{
int nshift = shifts.order;
(*this)(Linop,src,results);
psi = shifts.norm*src;
for(int i=0;i<nshift;i++){
psi = psi + shifts.residues[i]*results[i];
}
return;
}
void operator() (LinearOperatorBase<Field> &Linop, const Field &src, std::vector<Field> &psi)
{
GridBase *grid = src.Grid();
////////////////////////////////////////////////////////////////////////
// Convenience references to the info stored in "MultiShiftFunction"
////////////////////////////////////////////////////////////////////////
int nshift = shifts.order;
std::vector<RealD> &mass(shifts.poles); // Make references to array in "shifts"
std::vector<RealD> &mresidual(shifts.tolerances);
std::vector<RealD> alpha(nshift,1.0);
std::vector<Field> ps(nshift,grid);// Search directions
assert(psi.size()==nshift);
assert(mass.size()==nshift);
assert(mresidual.size()==nshift);
// dynamic sized arrays on stack; 2d is a pain with vector
RealD bs[nshift];
RealD rsq[nshift];
RealD z[nshift][2];
int converged[nshift];
const int primary =0;
//Primary shift fields CG iteration
RealD a,b,c,d;
RealD cp,bp,qq; //prev
// Matrix mult fields
Field r(grid);
Field p(grid);
Field tmp(grid);
Field mmp(grid);
// Check lightest mass
for(int s=0;s<nshift;s++){
assert( mass[s]>= mass[primary] );
converged[s]=0;
}
// Wire guess to zero
// Residuals "r" are src
// First search direction "p" is also src
cp = norm2(src);
for(int s=0;s<nshift;s++){
rsq[s] = cp * mresidual[s] * mresidual[s];
std::cout<<GridLogMessage<<"ConjugateGradientMultiShift: shift "<<s
<<" target resid "<<rsq[s]<<std::endl;
ps[s] = src;
}
// r and p for primary
r=src;
p=src;
//MdagM+m[0]
Linop.HermOpAndNorm(p,mmp,d,qq);
axpy(mmp,mass[0],p,mmp);
RealD rn = norm2(p);
d += rn*mass[0];
// have verified that inner product of
// p and mmp is equal to d after this since
// the d computation is tricky
// qq = real(innerProduct(p,mmp));
// std::cout<<GridLogMessage << "debug equal ? qq "<<qq<<" d "<< d<<std::endl;
b = -cp /d;
// Set up the various shift variables
int iz=0;
z[0][1-iz] = 1.0;
z[0][iz] = 1.0;
bs[0] = b;
for(int s=1;s<nshift;s++){
z[s][1-iz] = 1.0;
z[s][iz] = 1.0/( 1.0 - b*(mass[s]-mass[0]));
bs[s] = b*z[s][iz];
}
// r += b[0] A.p[0]
// c= norm(r)
c=axpy_norm(r,b,mmp,r);
for(int s=0;s<nshift;s++) {
axpby(psi[s],0.,-bs[s]*alpha[s],src,src);
}
///////////////////////////////////////
// Timers
///////////////////////////////////////
GridStopWatch AXPYTimer;
GridStopWatch ShiftTimer;
GridStopWatch QRTimer;
GridStopWatch MatrixTimer;
GridStopWatch SolverTimer;
SolverTimer.Start();
// Iteration loop
int k;
for (k=1;k<=MaxIterations;k++){
a = c /cp;
AXPYTimer.Start();
axpy(p,a,p,r);
AXPYTimer.Stop();
// Note to self - direction ps is iterated seperately
// for each shift. Does not appear to have any scope
// for avoiding linear algebra in "single" case.
//
// However SAME r is used. Could load "r" and update
// ALL ps[s]. 2/3 Bandwidth saving
// New Kernel: Load r, vector of coeffs, vector of pointers ps
AXPYTimer.Start();
for(int s=0;s<nshift;s++){
if ( ! converged[s] ) {
if (s==0){
axpy(ps[s],a,ps[s],r);
} else{
RealD as =a *z[s][iz]*bs[s] /(z[s][1-iz]*b);
axpby(ps[s],z[s][iz],as,r,ps[s]);
}
}
}
AXPYTimer.Stop();
cp=c;
MatrixTimer.Start();
//Linop.HermOpAndNorm(p,mmp,d,qq); // d is used
// The below is faster on KNL
Linop.HermOp(p,mmp);
d=real(innerProduct(p,mmp));
MatrixTimer.Stop();
AXPYTimer.Start();
axpy(mmp,mass[0],p,mmp);
AXPYTimer.Stop();
RealD rn = norm2(p);
d += rn*mass[0];
bp=b;
b=-cp/d;
AXPYTimer.Start();
c=axpy_norm(r,b,mmp,r);
AXPYTimer.Stop();
// Toggle the recurrence history
bs[0] = b;
iz = 1-iz;
ShiftTimer.Start();
for(int s=1;s<nshift;s++){
if((!converged[s])){
RealD z0 = z[s][1-iz];
RealD z1 = z[s][iz];
z[s][iz] = z0*z1*bp
/ (b*a*(z1-z0) + z1*bp*(1- (mass[s]-mass[0])*b));
bs[s] = b*z[s][iz]/z0; // NB sign rel to Mike
}
}
ShiftTimer.Stop();
for(int s=0;s<nshift;s++){
int ss = s;
// Scope for optimisation here in case of "single".
// Could load psi[0] and pull all ps[s] in.
// if ( single ) ss=primary;
// Bandwith saving in single case is Ls * 3 -> 2+Ls, so ~ 3x saving
// Pipelined CG gain:
//
// New Kernel: Load r, vector of coeffs, vector of pointers ps
// New Kernel: Load psi[0], vector of coeffs, vector of pointers ps
// If can predict the coefficient bs then we can fuse these and avoid write reread cyce
// on ps[s].
// Before: 3 x npole + 3 x npole
// After : 2 x npole (ps[s]) => 3x speed up of multishift CG.
if( (!converged[s]) ) {
axpy(psi[ss],-bs[s]*alpha[s],ps[s],psi[ss]);
}
}
// Convergence checks
int all_converged = 1;
for(int s=0;s<nshift;s++){
if ( (!converged[s]) ){
RealD css = c * z[s][iz]* z[s][iz];
if(css<rsq[s]){
if ( ! converged[s] )
std::cout<<GridLogMessage<<"ConjugateGradientMultiShift k="<<k<<" Shift "<<s<<" has converged"<<std::endl;
converged[s]=1;
} else {
all_converged=0;
}
}
}
if ( all_converged ){
SolverTimer.Stop();
std::cout<<GridLogMessage<< "CGMultiShift: All shifts have converged iteration "<<k<<std::endl;
std::cout<<GridLogMessage<< "CGMultiShift: Checking solutions"<<std::endl;
// Check answers
for(int s=0; s < nshift; s++) {
Linop.HermOpAndNorm(psi[s],mmp,d,qq);
axpy(tmp,mass[s],psi[s],mmp);
axpy(r,-alpha[s],src,tmp);
RealD rn = norm2(r);
RealD cn = norm2(src);
std::cout<<GridLogMessage<<"CGMultiShift: shift["<<s<<"] true residual "<<std::sqrt(rn/cn)<<std::endl;
}
std::cout << GridLogMessage << "Time Breakdown "<<std::endl;
std::cout << GridLogMessage << "\tElapsed " << SolverTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tAXPY " << AXPYTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tMarix " << MatrixTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tShift " << ShiftTimer.Elapsed() <<std::endl;
IterationsToComplete = k;
return;
}
}
// ugly hack
std::cout<<GridLogMessage<<"CG multi shift did not converge"<<std::endl;
// assert(0);
}
};
NAMESPACE_END(Grid);
#endif

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/ConjugateGradientReliableUpdate.h
Copyright (C) 2015
Author: Christopher Kelly <ckelly@phys.columbia.edu>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_CONJUGATE_GRADIENT_RELIABLE_UPDATE_H
#define GRID_CONJUGATE_GRADIENT_RELIABLE_UPDATE_H
NAMESPACE_BEGIN(Grid);
template<class FieldD,class FieldF,
typename std::enable_if< getPrecision<FieldD>::value == 2, int>::type = 0,
typename std::enable_if< getPrecision<FieldF>::value == 1, int>::type = 0>
class ConjugateGradientReliableUpdate : public LinearFunction<FieldD> {
public:
bool ErrorOnNoConverge; // throw an assert when the CG fails to converge.
// Defaults true.
RealD Tolerance;
Integer MaxIterations;
Integer IterationsToComplete; //Number of iterations the CG took to finish. Filled in upon completion
Integer ReliableUpdatesPerformed;
bool DoFinalCleanup; //Final DP cleanup, defaults to true
Integer IterationsToCleanup; //Final DP cleanup step iterations
LinearOperatorBase<FieldF> &Linop_f;
LinearOperatorBase<FieldD> &Linop_d;
GridBase* SinglePrecGrid;
RealD Delta; //reliable update parameter
//Optional ability to switch to a different linear operator once the tolerance reaches a certain point. Useful for single/half -> single/single
LinearOperatorBase<FieldF> *Linop_fallback;
RealD fallback_transition_tol;
ConjugateGradientReliableUpdate(RealD tol, Integer maxit, RealD _delta, GridBase* _sp_grid, LinearOperatorBase<FieldF> &_Linop_f, LinearOperatorBase<FieldD> &_Linop_d, bool err_on_no_conv = true)
: Tolerance(tol),
MaxIterations(maxit),
Delta(_delta),
Linop_f(_Linop_f),
Linop_d(_Linop_d),
SinglePrecGrid(_sp_grid),
ErrorOnNoConverge(err_on_no_conv),
DoFinalCleanup(true),
Linop_fallback(NULL)
{};
void setFallbackLinop(LinearOperatorBase<FieldF> &_Linop_fallback, const RealD _fallback_transition_tol){
Linop_fallback = &_Linop_fallback;
fallback_transition_tol = _fallback_transition_tol;
}
void operator()(const FieldD &src, FieldD &psi) {
LinearOperatorBase<FieldF> *Linop_f_use = &Linop_f;
bool using_fallback = false;
psi.Checkerboard() = src.Checkerboard();
conformable(psi, src);
RealD cp, c, a, d, b, ssq, qq, b_pred;
FieldD p(src);
FieldD mmp(src);
FieldD r(src);
// Initial residual computation & set up
RealD guess = norm2(psi);
assert(std::isnan(guess) == 0);
Linop_d.HermOpAndNorm(psi, mmp, d, b);
r = src - mmp;
p = r;
a = norm2(p);
cp = a;
ssq = norm2(src);
std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradientReliableUpdate: guess " << guess << std::endl;
std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradientReliableUpdate: src " << ssq << std::endl;
std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradientReliableUpdate: mp " << d << std::endl;
std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradientReliableUpdate: mmp " << b << std::endl;
std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradientReliableUpdate: cp,r " << cp << std::endl;
std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradientReliableUpdate: p " << a << std::endl;
RealD rsq = Tolerance * Tolerance * ssq;
// Check if guess is really REALLY good :)
if (cp <= rsq) {
std::cout << GridLogMessage << "ConjugateGradientReliableUpdate guess was REALLY good\n";
std::cout << GridLogMessage << "\tComputed residual " << std::sqrt(cp / ssq)<<std::endl;
return;
}
//Single prec initialization
FieldF r_f(SinglePrecGrid);
r_f.Checkerboard() = r.Checkerboard();
precisionChange(r_f, r);
FieldF psi_f(r_f);
psi_f = Zero();
FieldF p_f(r_f);
FieldF mmp_f(r_f);
RealD MaxResidSinceLastRelUp = cp; //initial residual
std::cout << GridLogIterative << std::setprecision(4)
<< "ConjugateGradient: k=0 residual " << cp << " target " << rsq << std::endl;
GridStopWatch LinalgTimer;
GridStopWatch MatrixTimer;
GridStopWatch SolverTimer;
SolverTimer.Start();
int k = 0;
int l = 0;
for (k = 1; k <= MaxIterations; k++) {
c = cp;
MatrixTimer.Start();
Linop_f_use->HermOpAndNorm(p_f, mmp_f, d, qq);
MatrixTimer.Stop();
LinalgTimer.Start();
a = c / d;
b_pred = a * (a * qq - d) / c;
cp = axpy_norm(r_f, -a, mmp_f, r_f);
b = cp / c;
// Fuse these loops ; should be really easy
psi_f = a * p_f + psi_f;
//p_f = p_f * b + r_f;
LinalgTimer.Stop();
std::cout << GridLogIterative << "ConjugateGradientReliableUpdate: Iteration " << k
<< " residual " << cp << " target " << rsq << std::endl;
std::cout << GridLogDebug << "a = "<< a << " b_pred = "<< b_pred << " b = "<< b << std::endl;
std::cout << GridLogDebug << "qq = "<< qq << " d = "<< d << " c = "<< c << std::endl;
if(cp > MaxResidSinceLastRelUp){
std::cout << GridLogIterative << "ConjugateGradientReliableUpdate: updating MaxResidSinceLastRelUp : " << MaxResidSinceLastRelUp << " -> " << cp << std::endl;
MaxResidSinceLastRelUp = cp;
}
// Stopping condition
if (cp <= rsq) {
//Although not written in the paper, I assume that I have to add on the final solution
precisionChange(mmp, psi_f);
psi = psi + mmp;
SolverTimer.Stop();
Linop_d.HermOpAndNorm(psi, mmp, d, qq);
p = mmp - src;
RealD srcnorm = std::sqrt(norm2(src));
RealD resnorm = std::sqrt(norm2(p));
RealD true_residual = resnorm / srcnorm;
std::cout << GridLogMessage << "ConjugateGradientReliableUpdate Converged on iteration " << k << " after " << l << " reliable updates" << std::endl;
std::cout << GridLogMessage << "\tComputed residual " << std::sqrt(cp / ssq)<<std::endl;
std::cout << GridLogMessage << "\tTrue residual " << true_residual<<std::endl;
std::cout << GridLogMessage << "\tTarget " << Tolerance << std::endl;
std::cout << GridLogMessage << "Time breakdown "<<std::endl;
std::cout << GridLogMessage << "\tElapsed " << SolverTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tMatrix " << MatrixTimer.Elapsed() <<std::endl;
std::cout << GridLogMessage << "\tLinalg " << LinalgTimer.Elapsed() <<std::endl;
IterationsToComplete = k;
ReliableUpdatesPerformed = l;
if(DoFinalCleanup){
//Do a final CG to cleanup
std::cout << GridLogMessage << "ConjugateGradientReliableUpdate performing final cleanup.\n";
ConjugateGradient<FieldD> CG(Tolerance,MaxIterations);
CG.ErrorOnNoConverge = ErrorOnNoConverge;
CG(Linop_d,src,psi);
IterationsToCleanup = CG.IterationsToComplete;
}
else if (ErrorOnNoConverge) assert(true_residual / Tolerance < 10000.0);
std::cout << GridLogMessage << "ConjugateGradientReliableUpdate complete.\n";
return;
}
else if(cp < Delta * MaxResidSinceLastRelUp) { //reliable update
std::cout << GridLogMessage << "ConjugateGradientReliableUpdate "
<< cp << "(residual) < " << Delta << "(Delta) * " << MaxResidSinceLastRelUp << "(MaxResidSinceLastRelUp) on iteration " << k << " : performing reliable update\n";
precisionChange(mmp, psi_f);
psi = psi + mmp;
Linop_d.HermOpAndNorm(psi, mmp, d, qq);
r = src - mmp;
psi_f = Zero();
precisionChange(r_f, r);
cp = norm2(r);
MaxResidSinceLastRelUp = cp;
b = cp/c;
std::cout << GridLogMessage << "ConjugateGradientReliableUpdate new residual " << cp << std::endl;
l = l+1;
}
p_f = p_f * b + r_f; //update search vector after reliable update appears to help convergence
if(!using_fallback && Linop_fallback != NULL && cp < fallback_transition_tol){
std::cout << GridLogMessage << "ConjugateGradientReliableUpdate switching to fallback linear operator on iteration " << k << " at residual " << cp << std::endl;
Linop_f_use = Linop_fallback;
using_fallback = true;
}
}
std::cout << GridLogMessage << "ConjugateGradientReliableUpdate did NOT converge"
<< std::endl;
if (ErrorOnNoConverge) assert(0);
IterationsToComplete = k;
ReliableUpdatesPerformed = l;
}
};
NAMESPACE_END(Grid);
#endif

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/ConjugateResidual.h
Copyright (C) 2015
Author: Azusa Yamaguchi <ayamaguc@staffmail.ed.ac.uk>
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_CONJUGATE_RESIDUAL_H
#define GRID_CONJUGATE_RESIDUAL_H
NAMESPACE_BEGIN(Grid);
/////////////////////////////////////////////////////////////
// Base classes for iterative processes based on operators
// single input vec, single output vec.
/////////////////////////////////////////////////////////////
template<class Field>
class ConjugateResidual : public OperatorFunction<Field> {
public:
RealD Tolerance;
Integer MaxIterations;
int verbose;
ConjugateResidual(RealD tol,Integer maxit) : Tolerance(tol), MaxIterations(maxit) {
verbose=0;
};
void operator() (LinearOperatorBase<Field> &Linop,const Field &src, Field &psi){
RealD a, b; // c, d;
RealD cp, ssq,rsq;
RealD rAr, rAAr, rArp;
RealD pAp, pAAp;
GridBase *grid = src.Grid();
psi=Zero();
Field r(grid), p(grid), Ap(grid), Ar(grid);
r=src;
p=src;
Linop.HermOpAndNorm(p,Ap,pAp,pAAp);
Linop.HermOpAndNorm(r,Ar,rAr,rAAr);
cp =norm2(r);
ssq=norm2(src);
rsq=Tolerance*Tolerance*ssq;
if (verbose) std::cout<<GridLogMessage<<"ConjugateResidual: iteration " <<0<<" residual "<<cp<< " target"<< rsq<<std::endl;
for(int k=1;k<MaxIterations;k++){
a = rAr/pAAp;
axpy(psi,a,p,psi);
cp = axpy_norm(r,-a,Ap,r);
rArp=rAr;
Linop.HermOpAndNorm(r,Ar,rAr,rAAr);
b =rAr/rArp;
axpy(p,b,p,r);
pAAp=axpy_norm(Ap,b,Ap,Ar);
if(verbose) std::cout<<GridLogMessage<<"ConjugateResidual: iteration " <<k<<" residual "<<cp<< " target"<< rsq<<std::endl;
if(cp<rsq) {
Linop.HermOp(psi,Ap);
axpy(r,-1.0,src,Ap);
RealD true_resid = norm2(r)/ssq;
std::cout<<GridLogMessage<<"ConjugateResidual: Converged on iteration " <<k
<< " computed residual "<<std::sqrt(cp/ssq)
<< " true residual "<<std::sqrt(true_resid)
<< " target " <<Tolerance <<std::endl;
return;
}
}
std::cout<<GridLogMessage<<"ConjugateResidual did NOT converge"<<std::endl;
assert(0);
}
};
NAMESPACE_END(Grid);
#endif

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/ImplicitlyRestartedLanczos.h
Copyright (C) 2015
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_DEFLATION_H
#define GRID_DEFLATION_H
namespace Grid {
template<class Field>
class ZeroGuesser: public LinearFunction<Field> {
public:
virtual void operator()(const Field &src, Field &guess) { guess = Zero(); };
};
template<class Field>
class SourceGuesser: public LinearFunction<Field> {
public:
virtual void operator()(const Field &src, Field &guess) { guess = src; };
};
////////////////////////////////
// Fine grid deflation
////////////////////////////////
template<class Field>
class DeflatedGuesser: public LinearFunction<Field> {
private:
const std::vector<Field> &evec;
const std::vector<RealD> &eval;
public:
DeflatedGuesser(const std::vector<Field> & _evec,const std::vector<RealD> & _eval) : evec(_evec), eval(_eval) {};
virtual void operator()(const Field &src,Field &guess) {
guess = Zero();
assert(evec.size()==eval.size());
auto N = evec.size();
for (int i=0;i<N;i++) {
const Field& tmp = evec[i];
axpy(guess,TensorRemove(innerProduct(tmp,src)) / eval[i],tmp,guess);
}
guess.Checkerboard() = src.Checkerboard();
}
};
template<class FineField, class CoarseField>
class LocalCoherenceDeflatedGuesser: public LinearFunction<FineField> {
private:
const std::vector<FineField> &subspace;
const std::vector<CoarseField> &evec_coarse;
const std::vector<RealD> &eval_coarse;
public:
LocalCoherenceDeflatedGuesser(const std::vector<FineField> &_subspace,
const std::vector<CoarseField> &_evec_coarse,
const std::vector<RealD> &_eval_coarse)
: subspace(_subspace),
evec_coarse(_evec_coarse),
eval_coarse(_eval_coarse)
{
}
void operator()(const FineField &src,FineField &guess) {
int N = (int)evec_coarse.size();
CoarseField src_coarse(evec_coarse[0].Grid());
CoarseField guess_coarse(evec_coarse[0].Grid()); guess_coarse = Zero();
blockProject(src_coarse,src,subspace);
for (int i=0;i<N;i++) {
const CoarseField & tmp = evec_coarse[i];
axpy(guess_coarse,TensorRemove(innerProduct(tmp,src_coarse)) / eval_coarse[i],tmp,guess_coarse);
}
blockPromote(guess_coarse,guess,subspace);
guess.Checkerboard() = src.Checkerboard();
};
};
}
#endif

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/ImplicitlyRestartedLanczos.h
Copyright (C) 2015
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
Author: paboyle <paboyle@ph.ed.ac.uk>
Author: Chulwoo Jung <chulwoo@bnl.gov>
Author: Christoph Lehner <clehner@bnl.gov>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_BIRL_H
#define GRID_BIRL_H
#include <string.h> //memset
//#include <zlib.h>
#include <sys/stat.h>
NAMESPACE_BEGIN(Grid);
////////////////////////////////////////////////////////
// Move following 100 LOC to lattice/Lattice_basis.h
////////////////////////////////////////////////////////
template<class Field>
void basisOrthogonalize(std::vector<Field> &basis,Field &w,int k)
{
for(int j=0; j<k; ++j){
auto ip = innerProduct(basis[j],w);
w = w - ip*basis[j];
}
}
template<class Field>
void basisRotate(std::vector<Field> &basis,Eigen::MatrixXd& Qt,int j0, int j1, int k0,int k1,int Nm)
{
typedef decltype(basis[0].View()) View;
auto tmp_v = basis[0].View();
std::vector<View> basis_v(basis.size(),tmp_v);
typedef typename Field::vector_object vobj;
GridBase* grid = basis[0].Grid();
for(int k=0;k<basis.size();k++){
basis_v[k] = basis[k].View();
}
thread_region
{
std::vector < vobj , commAllocator<vobj> > B(Nm); // Thread private
thread_loop_in_region( (int ss=0;ss < grid->oSites();ss++),{
for(int j=j0; j<j1; ++j) B[j]=0.;
for(int j=j0; j<j1; ++j){
for(int k=k0; k<k1; ++k){
B[j] +=Qt(j,k) * basis_v[k][ss];
}
}
for(int j=j0; j<j1; ++j){
basis_v[j][ss] = B[j];
}
});
}
}
// Extract a single rotated vector
template<class Field>
void basisRotateJ(Field &result,std::vector<Field> &basis,Eigen::MatrixXd& Qt,int j, int k0,int k1,int Nm)
{
typedef typename Field::vector_object vobj;
GridBase* grid = basis[0].Grid();
result.Checkerboard() = basis[0].Checkerboard();
auto result_v=result.View();
thread_loop( (int ss=0;ss < grid->oSites();ss++),{
vobj B = Zero();
for(int k=k0; k<k1; ++k){
auto basis_k = basis[k].View();
B +=Qt(j,k) * basis_k[ss];
}
result_v[ss] = B;
});
}
template<class Field>
void basisReorderInPlace(std::vector<Field> &_v,std::vector<RealD>& sort_vals, std::vector<int>& idx)
{
int vlen = idx.size();
assert(vlen>=1);
assert(vlen<=sort_vals.size());
assert(vlen<=_v.size());
for (size_t i=0;i<vlen;i++) {
if (idx[i] != i) {
//////////////////////////////////////
// idx[i] is a table of desired sources giving a permutation.
// Swap v[i] with v[idx[i]].
// Find j>i for which _vnew[j] = _vold[i],
// track the move idx[j] => idx[i]
// track the move idx[i] => i
//////////////////////////////////////
size_t j;
for (j=i;j<idx.size();j++)
if (idx[j]==i)
break;
assert(idx[i] > i); assert(j!=idx.size()); assert(idx[j]==i);
swap(_v[i],_v[idx[i]]); // should use vector move constructor, no data copy
std::swap(sort_vals[i],sort_vals[idx[i]]);
idx[j] = idx[i];
idx[i] = i;
}
}
}
inline std::vector<int> basisSortGetIndex(std::vector<RealD>& sort_vals)
{
std::vector<int> idx(sort_vals.size());
std::iota(idx.begin(), idx.end(), 0);
// sort indexes based on comparing values in v
std::sort(idx.begin(), idx.end(), [&sort_vals](int i1, int i2) {
return ::fabs(sort_vals[i1]) < ::fabs(sort_vals[i2]);
});
return idx;
}
template<class Field>
void basisSortInPlace(std::vector<Field> & _v,std::vector<RealD>& sort_vals, bool reverse)
{
std::vector<int> idx = basisSortGetIndex(sort_vals);
if (reverse)
std::reverse(idx.begin(), idx.end());
basisReorderInPlace(_v,sort_vals,idx);
}
// PAB: faster to compute the inner products first then fuse loops.
// If performance critical can improve.
template<class Field>
void basisDeflate(const std::vector<Field> &_v,const std::vector<RealD>& eval,const Field& src_orig,Field& result) {
result = Zero();
assert(_v.size()==eval.size());
int N = (int)_v.size();
for (int i=0;i<N;i++) {
Field& tmp = _v[i];
axpy(result,TensorRemove(innerProduct(tmp,src_orig)) / eval[i],tmp,result);
}
}
/////////////////////////////////////////////////////////////
// Implicitly restarted lanczos
/////////////////////////////////////////////////////////////
template<class Field> class ImplicitlyRestartedLanczosTester
{
public:
virtual int TestConvergence(int j,RealD resid,Field &evec, RealD &eval,RealD evalMaxApprox)=0;
virtual int ReconstructEval(int j,RealD resid,Field &evec, RealD &eval,RealD evalMaxApprox)=0;
};
enum IRLdiagonalisation {
IRLdiagonaliseWithDSTEGR,
IRLdiagonaliseWithQR,
IRLdiagonaliseWithEigen
};
template<class Field> class ImplicitlyRestartedLanczosHermOpTester : public ImplicitlyRestartedLanczosTester<Field>
{
public:
LinearFunction<Field> &_HermOp;
ImplicitlyRestartedLanczosHermOpTester(LinearFunction<Field> &HermOp) : _HermOp(HermOp) { };
int ReconstructEval(int j,RealD resid,Field &B, RealD &eval,RealD evalMaxApprox)
{
return TestConvergence(j,resid,B,eval,evalMaxApprox);
}
int TestConvergence(int j,RealD eresid,Field &B, RealD &eval,RealD evalMaxApprox)
{
Field v(B);
RealD eval_poly = eval;
// Apply operator
_HermOp(B,v);
RealD vnum = real(innerProduct(B,v)); // HermOp.
RealD vden = norm2(B);
RealD vv0 = norm2(v);
eval = vnum/vden;
v -= eval*B;
RealD vv = norm2(v) / ::pow(evalMaxApprox,2.0);
std::cout.precision(13);
std::cout<<GridLogIRL << "[" << std::setw(3)<<j<<"] "
<<"eval = "<<std::setw(25)<< eval << " (" << eval_poly << ")"
<<" |H B[i] - eval[i]B[i]|^2 / evalMaxApprox^2 " << std::setw(25) << vv
<<std::endl;
int conv=0;
if( (vv<eresid*eresid) ) conv = 1;
return conv;
}
};
template<class Field>
class ImplicitlyRestartedLanczos {
private:
const RealD small = 1.0e-8;
int MaxIter;
int MinRestart; // Minimum number of restarts; only check for convergence after
int Nstop; // Number of evecs checked for convergence
int Nk; // Number of converged sought
// int Np; // Np -- Number of spare vecs in krylov space // == Nm - Nk
int Nm; // Nm -- total number of vectors
IRLdiagonalisation diagonalisation;
int orth_period;
RealD OrthoTime;
RealD eresid, betastp;
////////////////////////////////
// Embedded objects
////////////////////////////////
LinearFunction<Field> &_PolyOp;
LinearFunction<Field> &_HermOp;
ImplicitlyRestartedLanczosTester<Field> &_Tester;
// Default tester provided (we need a ref to something in default case)
ImplicitlyRestartedLanczosHermOpTester<Field> SimpleTester;
/////////////////////////
// Constructor
/////////////////////////
public:
//////////////////////////////////////////////////////////////////
// PAB:
//////////////////////////////////////////////////////////////////
// Too many options & knobs.
// Eliminate:
// orth_period
// betastp
// MinRestart
//
// Do we really need orth_period
// What is the theoretical basis & guarantees of betastp ?
// Nstop=Nk viable?
// MinRestart avoidable with new convergence test?
// Could cut to PolyOp, HermOp, Tester, Nk, Nm, resid, maxiter (+diagonalisation)
// HermOp could be eliminated if we dropped the Power method for max eval.
// -- also: The eval, eval2, eval2_copy stuff is still unnecessarily unclear
//////////////////////////////////////////////////////////////////
ImplicitlyRestartedLanczos(LinearFunction<Field> & PolyOp,
LinearFunction<Field> & HermOp,
ImplicitlyRestartedLanczosTester<Field> & Tester,
int _Nstop, // sought vecs
int _Nk, // sought vecs
int _Nm, // spare vecs
RealD _eresid, // resid in lmdue deficit
int _MaxIter, // Max iterations
RealD _betastp=0.0, // if beta(k) < betastp: converged
int _MinRestart=1, int _orth_period = 1,
IRLdiagonalisation _diagonalisation= IRLdiagonaliseWithEigen) :
SimpleTester(HermOp), _PolyOp(PolyOp), _HermOp(HermOp), _Tester(Tester),
Nstop(_Nstop) , Nk(_Nk), Nm(_Nm),
eresid(_eresid), betastp(_betastp),
MaxIter(_MaxIter) , MinRestart(_MinRestart),
orth_period(_orth_period), diagonalisation(_diagonalisation) { };
ImplicitlyRestartedLanczos(LinearFunction<Field> & PolyOp,
LinearFunction<Field> & HermOp,
int _Nstop, // sought vecs
int _Nk, // sought vecs
int _Nm, // spare vecs
RealD _eresid, // resid in lmdue deficit
int _MaxIter, // Max iterations
RealD _betastp=0.0, // if beta(k) < betastp: converged
int _MinRestart=1, int _orth_period = 1,
IRLdiagonalisation _diagonalisation= IRLdiagonaliseWithEigen) :
SimpleTester(HermOp), _PolyOp(PolyOp), _HermOp(HermOp), _Tester(SimpleTester),
Nstop(_Nstop) , Nk(_Nk), Nm(_Nm),
eresid(_eresid), betastp(_betastp),
MaxIter(_MaxIter) , MinRestart(_MinRestart),
orth_period(_orth_period), diagonalisation(_diagonalisation) { };
////////////////////////////////
// Helpers
////////////////////////////////
template<typename T> static RealD normalise(T& v)
{
RealD nn = norm2(v);
nn = std::sqrt(nn);
v = v * (1.0/nn);
return nn;
}
void orthogonalize(Field& w, std::vector<Field>& evec,int k)
{
OrthoTime-=usecond()/1e6;
basisOrthogonalize(evec,w,k);
normalise(w);
OrthoTime+=usecond()/1e6;
}
/* Rudy Arthur's thesis pp.137
------------------------
Require: M > K P = M K †
Compute the factorization AVM = VM HM + fM eM
repeat
Q=I
for i = 1,...,P do
QiRi =HM θiI Q = QQi
H M = Q †i H M Q i
end for
βK =HM(K+1,K) σK =Q(M,K)
r=vK+1βK +rσK
VK =VM(1:M)Q(1:M,1:K)
HK =HM(1:K,1:K)
→AVK =VKHK +fKe†K † Extend to an M = K + P step factorization AVM = VMHM + fMeM
until convergence
*/
void calc(std::vector<RealD>& eval, std::vector<Field>& evec, const Field& src, int& Nconv, bool reverse=false)
{
GridBase *grid = src.Grid();
assert(grid == evec[0].Grid());
GridLogIRL.TimingMode(1);
std::cout << GridLogIRL <<"**************************************************************************"<< std::endl;
std::cout << GridLogIRL <<" ImplicitlyRestartedLanczos::calc() starting iteration 0 / "<< MaxIter<< std::endl;
std::cout << GridLogIRL <<"**************************************************************************"<< std::endl;
std::cout << GridLogIRL <<" -- seek Nk = " << Nk <<" vectors"<< std::endl;
std::cout << GridLogIRL <<" -- accept Nstop = " << Nstop <<" vectors"<< std::endl;
std::cout << GridLogIRL <<" -- total Nm = " << Nm <<" vectors"<< std::endl;
std::cout << GridLogIRL <<" -- size of eval = " << eval.size() << std::endl;
std::cout << GridLogIRL <<" -- size of evec = " << evec.size() << std::endl;
if ( diagonalisation == IRLdiagonaliseWithDSTEGR ) {
std::cout << GridLogIRL << "Diagonalisation is DSTEGR "<<std::endl;
} else if ( diagonalisation == IRLdiagonaliseWithQR ) {
std::cout << GridLogIRL << "Diagonalisation is QR "<<std::endl;
} else if ( diagonalisation == IRLdiagonaliseWithEigen ) {
std::cout << GridLogIRL << "Diagonalisation is Eigen "<<std::endl;
}
std::cout << GridLogIRL <<"**************************************************************************"<< std::endl;
assert(Nm <= evec.size() && Nm <= eval.size());
// quickly get an idea of the largest eigenvalue to more properly normalize the residuum
RealD evalMaxApprox = 0.0;
{
auto src_n = src;
auto tmp = src;
const int _MAX_ITER_IRL_MEVAPP_ = 50;
for (int i=0;i<_MAX_ITER_IRL_MEVAPP_;i++) {
normalise(src_n);
_HermOp(src_n,tmp);
RealD vnum = real(innerProduct(src_n,tmp)); // HermOp.
RealD vden = norm2(src_n);
RealD na = vnum/vden;
if (fabs(evalMaxApprox/na - 1.0) < 0.05)
i=_MAX_ITER_IRL_MEVAPP_;
evalMaxApprox = na;
std::cout << GridLogIRL << " Approximation of largest eigenvalue: " << evalMaxApprox << std::endl;
src_n = tmp;
}
}
std::vector<RealD> lme(Nm);
std::vector<RealD> lme2(Nm);
std::vector<RealD> eval2(Nm);
std::vector<RealD> eval2_copy(Nm);
Eigen::MatrixXd Qt = Eigen::MatrixXd::Zero(Nm,Nm);
Field f(grid);
Field v(grid);
int k1 = 1;
int k2 = Nk;
RealD beta_k;
Nconv = 0;
// Set initial vector
evec[0] = src;
normalise(evec[0]);
// Initial Nk steps
OrthoTime=0.;
for(int k=0; k<Nk; ++k) step(eval,lme,evec,f,Nm,k);
std::cout<<GridLogIRL <<"Initial "<< Nk <<"steps done "<<std::endl;
std::cout<<GridLogIRL <<"Initial steps:OrthoTime "<<OrthoTime<< "seconds"<<std::endl;
//////////////////////////////////
// Restarting loop begins
//////////////////////////////////
int iter;
for(iter = 0; iter<MaxIter; ++iter){
OrthoTime=0.;
std::cout<< GridLogMessage <<" **********************"<< std::endl;
std::cout<< GridLogMessage <<" Restart iteration = "<< iter << std::endl;
std::cout<< GridLogMessage <<" **********************"<< std::endl;
std::cout<<GridLogIRL <<" running "<<Nm-Nk <<" steps: "<<std::endl;
for(int k=Nk; k<Nm; ++k) step(eval,lme,evec,f,Nm,k);
f *= lme[Nm-1];
std::cout<<GridLogIRL <<" "<<Nm-Nk <<" steps done "<<std::endl;
std::cout<<GridLogIRL <<"Initial steps:OrthoTime "<<OrthoTime<< "seconds"<<std::endl;
//////////////////////////////////
// getting eigenvalues
//////////////////////////////////
for(int k=0; k<Nm; ++k){
eval2[k] = eval[k+k1-1];
lme2[k] = lme[k+k1-1];
}
Qt = Eigen::MatrixXd::Identity(Nm,Nm);
diagonalize(eval2,lme2,Nm,Nm,Qt,grid);
std::cout<<GridLogIRL <<" diagonalized "<<std::endl;
//////////////////////////////////
// sorting
//////////////////////////////////
eval2_copy = eval2;
std::partial_sort(eval2.begin(),eval2.begin()+Nm,eval2.end(),std::greater<RealD>());
std::cout<<GridLogIRL <<" evals sorted "<<std::endl;
const int chunk=8;
for(int io=0; io<k2;io+=chunk){
std::cout<<GridLogIRL << "eval "<< std::setw(3) << io ;
for(int ii=0;ii<chunk;ii++){
if ( (io+ii)<k2 )
std::cout<< " "<< std::setw(12)<< eval2[io+ii];
}
std::cout << std::endl;
}
//////////////////////////////////
// Implicitly shifted QR transformations
//////////////////////////////////
Qt = Eigen::MatrixXd::Identity(Nm,Nm);
for(int ip=k2; ip<Nm; ++ip){
QR_decomp(eval,lme,Nm,Nm,Qt,eval2[ip],k1,Nm);
}
std::cout<<GridLogIRL <<"QR decomposed "<<std::endl;
assert(k2<Nm); assert(k2<Nm); assert(k1>0);
basisRotate(evec,Qt,k1-1,k2+1,0,Nm,Nm); /// big constraint on the basis
std::cout<<GridLogIRL <<"basisRotated by Qt *"<<k1-1<<","<<k2+1<<")"<<std::endl;
////////////////////////////////////////////////////
// Compressed vector f and beta(k2)
////////////////////////////////////////////////////
f *= Qt(k2-1,Nm-1);
f += lme[k2-1] * evec[k2];
beta_k = norm2(f);
beta_k = std::sqrt(beta_k);
std::cout<<GridLogIRL<<" beta(k) = "<<beta_k<<std::endl;
RealD betar = 1.0/beta_k;
evec[k2] = betar * f;
lme[k2-1] = beta_k;
////////////////////////////////////////////////////
// Convergence test
////////////////////////////////////////////////////
for(int k=0; k<Nm; ++k){
eval2[k] = eval[k];
lme2[k] = lme[k];
}
Qt = Eigen::MatrixXd::Identity(Nm,Nm);
diagonalize(eval2,lme2,Nk,Nm,Qt,grid);
std::cout<<GridLogIRL <<" Diagonalized "<<std::endl;
Nconv = 0;
if (iter >= MinRestart) {
std::cout << GridLogIRL << "Test convergence: rotate subset of vectors to test convergence " << std::endl;
Field B(grid); B.Checkerboard() = evec[0].Checkerboard();
// power of two search pattern; not every evalue in eval2 is assessed.
int allconv =1;
for(int jj = 1; jj<=Nstop; jj*=2){
int j = Nstop-jj;
RealD e = eval2_copy[j]; // Discard the evalue
basisRotateJ(B,evec,Qt,j,0,Nk,Nm);
if( !_Tester.TestConvergence(j,eresid,B,e,evalMaxApprox) ) {
allconv=0;
}
}
// Do evec[0] for good measure
{
int j=0;
RealD e = eval2_copy[0];
basisRotateJ(B,evec,Qt,j,0,Nk,Nm);
if( !_Tester.TestConvergence(j,eresid,B,e,evalMaxApprox) ) allconv=0;
}
if ( allconv ) Nconv = Nstop;
// test if we converged, if so, terminate
std::cout<<GridLogIRL<<" #modes converged: >= "<<Nconv<<"/"<<Nstop<<std::endl;
// if( Nconv>=Nstop || beta_k < betastp){
if( Nconv>=Nstop){
goto converged;
}
} else {
std::cout << GridLogIRL << "iter < MinRestart: do not yet test for convergence\n";
} // end of iter loop
}
std::cout<<GridLogError<<"\n NOT converged.\n";
abort();
converged:
{
Field B(grid); B.Checkerboard() = evec[0].Checkerboard();
basisRotate(evec,Qt,0,Nk,0,Nk,Nm);
std::cout << GridLogIRL << " Rotated basis"<<std::endl;
Nconv=0;
//////////////////////////////////////////////////////////////////////
// Full final convergence test; unconditionally applied
//////////////////////////////////////////////////////////////////////
for(int j = 0; j<=Nk; j++){
B=evec[j];
if( _Tester.ReconstructEval(j,eresid,B,eval2[j],evalMaxApprox) ) {
Nconv++;
}
}
if ( Nconv < Nstop )
std::cout << GridLogIRL << "Nconv ("<<Nconv<<") < Nstop ("<<Nstop<<")"<<std::endl;
eval=eval2;
//Keep only converged
eval.resize(Nconv);// Nstop?
evec.resize(Nconv,grid);// Nstop?
basisSortInPlace(evec,eval,reverse);
}
std::cout << GridLogIRL <<"**************************************************************************"<< std::endl;
std::cout << GridLogIRL << "ImplicitlyRestartedLanczos CONVERGED ; Summary :\n";
std::cout << GridLogIRL <<"**************************************************************************"<< std::endl;
std::cout << GridLogIRL << " -- Iterations = "<< iter << "\n";
std::cout << GridLogIRL << " -- beta(k) = "<< beta_k << "\n";
std::cout << GridLogIRL << " -- Nconv = "<< Nconv << "\n";
std::cout << GridLogIRL <<"**************************************************************************"<< std::endl;
}
private:
/* Saad PP. 195
1. Choose an initial vector v1 of 2-norm unity. Set β1 ≡ 0, v0 ≡ 0
2. For k = 1,2,...,m Do:
3. wk:=Avkβkv_{k1}
4. αk:=(wk,vk) //
5. wk:=wkαkvk // wk orthog vk
6. βk+1 := ∥wk∥2. If βk+1 = 0 then Stop
7. vk+1 := wk/βk+1
8. EndDo
*/
void step(std::vector<RealD>& lmd,
std::vector<RealD>& lme,
std::vector<Field>& evec,
Field& w,int Nm,int k)
{
const RealD tiny = 1.0e-20;
assert( k< Nm );
GridStopWatch gsw_op,gsw_o;
Field& evec_k = evec[k];
_PolyOp(evec_k,w); std::cout<<GridLogIRL << "PolyOp" <<std::endl;
if(k>0) w -= lme[k-1] * evec[k-1];
ComplexD zalph = innerProduct(evec_k,w); // 4. αk:=(wk,vk)
RealD alph = real(zalph);
w = w - alph * evec_k;// 5. wk:=wkαkvk
RealD beta = normalise(w); // 6. βk+1 := ∥wk∥2. If βk+1 = 0 then Stop
// 7. vk+1 := wk/βk+1
lmd[k] = alph;
lme[k] = beta;
if (k>0 && k % orth_period == 0) {
orthogonalize(w,evec,k); // orthonormalise
std::cout<<GridLogIRL << "Orthogonalised " <<std::endl;
}
if(k < Nm-1) evec[k+1] = w;
std::cout<<GridLogIRL << "alpha[" << k << "] = " << zalph << " beta[" << k << "] = "<<beta<<std::endl;
if ( beta < tiny )
std::cout<<GridLogIRL << " beta is tiny "<<beta<<std::endl;
}
void diagonalize_Eigen(std::vector<RealD>& lmd, std::vector<RealD>& lme,
int Nk, int Nm,
Eigen::MatrixXd & Qt, // Nm x Nm
GridBase *grid)
{
Eigen::MatrixXd TriDiag = Eigen::MatrixXd::Zero(Nk,Nk);
for(int i=0;i<Nk;i++) TriDiag(i,i) = lmd[i];
for(int i=0;i<Nk-1;i++) TriDiag(i,i+1) = lme[i];
for(int i=0;i<Nk-1;i++) TriDiag(i+1,i) = lme[i];
Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> eigensolver(TriDiag);
for (int i = 0; i < Nk; i++) {
lmd[Nk-1-i] = eigensolver.eigenvalues()(i);
}
for (int i = 0; i < Nk; i++) {
for (int j = 0; j < Nk; j++) {
Qt(Nk-1-i,j) = eigensolver.eigenvectors()(j,i);
}
}
}
///////////////////////////////////////////////////////////////////////////
// File could end here if settle on Eigen ??? !!!
///////////////////////////////////////////////////////////////////////////
void QR_decomp(std::vector<RealD>& lmd, // Nm
std::vector<RealD>& lme, // Nm
int Nk, int Nm, // Nk, Nm
Eigen::MatrixXd& Qt, // Nm x Nm matrix
RealD Dsh, int kmin, int kmax)
{
int k = kmin-1;
RealD x;
RealD Fden = 1.0/hypot(lmd[k]-Dsh,lme[k]);
RealD c = ( lmd[k] -Dsh) *Fden;
RealD s = -lme[k] *Fden;
RealD tmpa1 = lmd[k];
RealD tmpa2 = lmd[k+1];
RealD tmpb = lme[k];
lmd[k] = c*c*tmpa1 +s*s*tmpa2 -2.0*c*s*tmpb;
lmd[k+1] = s*s*tmpa1 +c*c*tmpa2 +2.0*c*s*tmpb;
lme[k] = c*s*(tmpa1-tmpa2) +(c*c-s*s)*tmpb;
x =-s*lme[k+1];
lme[k+1] = c*lme[k+1];
for(int i=0; i<Nk; ++i){
RealD Qtmp1 = Qt(k,i);
RealD Qtmp2 = Qt(k+1,i);
Qt(k,i) = c*Qtmp1 - s*Qtmp2;
Qt(k+1,i)= s*Qtmp1 + c*Qtmp2;
}
// Givens transformations
for(int k = kmin; k < kmax-1; ++k){
RealD Fden = 1.0/hypot(x,lme[k-1]);
RealD c = lme[k-1]*Fden;
RealD s = - x*Fden;
RealD tmpa1 = lmd[k];
RealD tmpa2 = lmd[k+1];
RealD tmpb = lme[k];
lmd[k] = c*c*tmpa1 +s*s*tmpa2 -2.0*c*s*tmpb;
lmd[k+1] = s*s*tmpa1 +c*c*tmpa2 +2.0*c*s*tmpb;
lme[k] = c*s*(tmpa1-tmpa2) +(c*c-s*s)*tmpb;
lme[k-1] = c*lme[k-1] -s*x;
if(k != kmax-2){
x = -s*lme[k+1];
lme[k+1] = c*lme[k+1];
}
for(int i=0; i<Nk; ++i){
RealD Qtmp1 = Qt(k,i);
RealD Qtmp2 = Qt(k+1,i);
Qt(k,i) = c*Qtmp1 -s*Qtmp2;
Qt(k+1,i) = s*Qtmp1 +c*Qtmp2;
}
}
}
void diagonalize(std::vector<RealD>& lmd, std::vector<RealD>& lme,
int Nk, int Nm,
Eigen::MatrixXd & Qt,
GridBase *grid)
{
Qt = Eigen::MatrixXd::Identity(Nm,Nm);
if ( diagonalisation == IRLdiagonaliseWithDSTEGR ) {
diagonalize_lapack(lmd,lme,Nk,Nm,Qt,grid);
} else if ( diagonalisation == IRLdiagonaliseWithQR ) {
diagonalize_QR(lmd,lme,Nk,Nm,Qt,grid);
} else if ( diagonalisation == IRLdiagonaliseWithEigen ) {
diagonalize_Eigen(lmd,lme,Nk,Nm,Qt,grid);
} else {
assert(0);
}
}
#ifdef USE_LAPACK
void LAPACK_dstegr(char *jobz, char *range, int *n, double *d, double *e,
double *vl, double *vu, int *il, int *iu, double *abstol,
int *m, double *w, double *z, int *ldz, int *isuppz,
double *work, int *lwork, int *iwork, int *liwork,
int *info);
#endif
void diagonalize_lapack(std::vector<RealD>& lmd,
std::vector<RealD>& lme,
int Nk, int Nm,
Eigen::MatrixXd& Qt,
GridBase *grid)
{
#ifdef USE_LAPACK
const int size = Nm;
int NN = Nk;
double evals_tmp[NN];
double evec_tmp[NN][NN];
memset(evec_tmp[0],0,sizeof(double)*NN*NN);
double DD[NN];
double EE[NN];
for (int i = 0; i< NN; i++) {
for (int j = i - 1; j <= i + 1; j++) {
if ( j < NN && j >= 0 ) {
if (i==j) DD[i] = lmd[i];
if (i==j) evals_tmp[i] = lmd[i];
if (j==(i-1)) EE[j] = lme[j];
}
}
}
int evals_found;
int lwork = ( (18*NN) > (1+4*NN+NN*NN)? (18*NN):(1+4*NN+NN*NN)) ;
int liwork = 3+NN*10 ;
int iwork[liwork];
double work[lwork];
int isuppz[2*NN];
char jobz = 'V'; // calculate evals & evecs
char range = 'I'; // calculate all evals
// char range = 'A'; // calculate all evals
char uplo = 'U'; // refer to upper half of original matrix
char compz = 'I'; // Compute eigenvectors of tridiagonal matrix
int ifail[NN];
int info;
int total = grid->_Nprocessors;
int node = grid->_processor;
int interval = (NN/total)+1;
double vl = 0.0, vu = 0.0;
int il = interval*node+1 , iu = interval*(node+1);
if (iu > NN) iu=NN;
double tol = 0.0;
if (1) {
memset(evals_tmp,0,sizeof(double)*NN);
if ( il <= NN){
LAPACK_dstegr(&jobz, &range, &NN,
(double*)DD, (double*)EE,
&vl, &vu, &il, &iu, // these four are ignored if second parameteris 'A'
&tol, // tolerance
&evals_found, evals_tmp, (double*)evec_tmp, &NN,
isuppz,
work, &lwork, iwork, &liwork,
&info);
for (int i = iu-1; i>= il-1; i--){
evals_tmp[i] = evals_tmp[i - (il-1)];
if (il>1) evals_tmp[i-(il-1)]=0.;
for (int j = 0; j< NN; j++){
evec_tmp[i][j] = evec_tmp[i - (il-1)][j];
if (il>1) evec_tmp[i-(il-1)][j]=0.;
}
}
}
{
grid->GlobalSumVector(evals_tmp,NN);
grid->GlobalSumVector((double*)evec_tmp,NN*NN);
}
}
// Safer to sort instead of just reversing it,
// but the document of the routine says evals are sorted in increasing order.
// qr gives evals in decreasing order.
for(int i=0;i<NN;i++){
lmd [NN-1-i]=evals_tmp[i];
for(int j=0;j<NN;j++){
Qt((NN-1-i),j)=evec_tmp[i][j];
}
}
#else
assert(0);
#endif
}
void diagonalize_QR(std::vector<RealD>& lmd, std::vector<RealD>& lme,
int Nk, int Nm,
Eigen::MatrixXd & Qt,
GridBase *grid)
{
int QRiter = 100*Nm;
int kmin = 1;
int kmax = Nk;
// (this should be more sophisticated)
for(int iter=0; iter<QRiter; ++iter){
// determination of 2x2 leading submatrix
RealD dsub = lmd[kmax-1]-lmd[kmax-2];
RealD dd = std::sqrt(dsub*dsub + 4.0*lme[kmax-2]*lme[kmax-2]);
RealD Dsh = 0.5*(lmd[kmax-2]+lmd[kmax-1] +dd*(dsub/fabs(dsub)));
// (Dsh: shift)
// transformation
QR_decomp(lmd,lme,Nk,Nm,Qt,Dsh,kmin,kmax); // Nk, Nm
// Convergence criterion (redef of kmin and kamx)
for(int j=kmax-1; j>= kmin; --j){
RealD dds = fabs(lmd[j-1])+fabs(lmd[j]);
if(fabs(lme[j-1])+dds > dds){
kmax = j+1;
goto continued;
}
}
QRiter = iter;
return;
continued:
for(int j=0; j<kmax-1; ++j){
RealD dds = fabs(lmd[j])+fabs(lmd[j+1]);
if(fabs(lme[j])+dds > dds){
kmin = j+1;
break;
}
}
}
std::cout << GridLogError << "[QL method] Error - Too many iteration: "<<QRiter<<"\n";
abort();
}
};
NAMESPACE_END(Grid);
#endif

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@ -0,0 +1,405 @@
/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/LocalCoherenceLanczos.h
Copyright (C) 2015
Author: Christoph Lehner <clehner@bnl.gov>
Author: paboyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_LOCAL_COHERENCE_IRL_H
#define GRID_LOCAL_COHERENCE_IRL_H
NAMESPACE_BEGIN(Grid);
struct LanczosParams : Serializable {
public:
GRID_SERIALIZABLE_CLASS_MEMBERS(LanczosParams,
ChebyParams, Cheby,/*Chebyshev*/
int, Nstop, /*Vecs in Lanczos must converge Nstop < Nk < Nm*/
int, Nk, /*Vecs in Lanczos seek converge*/
int, Nm, /*Total vecs in Lanczos include restart*/
RealD, resid, /*residual*/
int, MaxIt,
RealD, betastp, /* ? */
int, MinRes); // Must restart
};
struct LocalCoherenceLanczosParams : Serializable {
public:
GRID_SERIALIZABLE_CLASS_MEMBERS(LocalCoherenceLanczosParams,
bool, saveEvecs,
bool, doFine,
bool, doFineRead,
bool, doCoarse,
bool, doCoarseRead,
LanczosParams, FineParams,
LanczosParams, CoarseParams,
ChebyParams, Smoother,
RealD , coarse_relax_tol,
std::vector<int>, blockSize,
std::string, config,
std::vector < ComplexD >, omega,
RealD, mass,
RealD, M5);
};
// Duplicate functionality; ProjectedFunctionHermOp could be used with the trivial function
template<class Fobj,class CComplex,int nbasis>
class ProjectedHermOp : public LinearFunction<Lattice<iVector<CComplex,nbasis > > > {
public:
typedef iVector<CComplex,nbasis > CoarseSiteVector;
typedef Lattice<CoarseSiteVector> CoarseField;
typedef Lattice<CComplex> CoarseScalar; // used for inner products on fine field
typedef Lattice<Fobj> FineField;
LinearOperatorBase<FineField> &_Linop;
std::vector<FineField> &subspace;
ProjectedHermOp(LinearOperatorBase<FineField>& linop, std::vector<FineField> & _subspace) :
_Linop(linop), subspace(_subspace)
{
assert(subspace.size() >0);
};
void operator()(const CoarseField& in, CoarseField& out) {
GridBase *FineGrid = subspace[0].Grid();
int checkerboard = subspace[0].Checkerboard();
FineField fin (FineGrid); fin.Checkerboard()= checkerboard;
FineField fout(FineGrid); fout.Checkerboard() = checkerboard;
blockPromote(in,fin,subspace); std::cout<<GridLogIRL<<"ProjectedHermop : Promote to fine"<<std::endl;
_Linop.HermOp(fin,fout); std::cout<<GridLogIRL<<"ProjectedHermop : HermOp (fine) "<<std::endl;
blockProject(out,fout,subspace); std::cout<<GridLogIRL<<"ProjectedHermop : Project to coarse "<<std::endl;
}
};
template<class Fobj,class CComplex,int nbasis>
class ProjectedFunctionHermOp : public LinearFunction<Lattice<iVector<CComplex,nbasis > > > {
public:
typedef iVector<CComplex,nbasis > CoarseSiteVector;
typedef Lattice<CoarseSiteVector> CoarseField;
typedef Lattice<CComplex> CoarseScalar; // used for inner products on fine field
typedef Lattice<Fobj> FineField;
OperatorFunction<FineField> & _poly;
LinearOperatorBase<FineField> &_Linop;
std::vector<FineField> &subspace;
ProjectedFunctionHermOp(OperatorFunction<FineField> & poly,
LinearOperatorBase<FineField>& linop,
std::vector<FineField> & _subspace) :
_poly(poly),
_Linop(linop),
subspace(_subspace)
{ };
void operator()(const CoarseField& in, CoarseField& out) {
GridBase *FineGrid = subspace[0].Grid();
int checkerboard = subspace[0].Checkerboard();
FineField fin (FineGrid); fin.Checkerboard() =checkerboard;
FineField fout(FineGrid);fout.Checkerboard() =checkerboard;
blockPromote(in,fin,subspace); std::cout<<GridLogIRL<<"ProjectedFunctionHermop : Promote to fine"<<std::endl;
_poly(_Linop,fin,fout); std::cout<<GridLogIRL<<"ProjectedFunctionHermop : Poly "<<std::endl;
blockProject(out,fout,subspace); std::cout<<GridLogIRL<<"ProjectedFunctionHermop : Project to coarse "<<std::endl;
}
};
template<class Fobj,class CComplex,int nbasis>
class ImplicitlyRestartedLanczosSmoothedTester : public ImplicitlyRestartedLanczosTester<Lattice<iVector<CComplex,nbasis > > >
{
public:
typedef iVector<CComplex,nbasis > CoarseSiteVector;
typedef Lattice<CoarseSiteVector> CoarseField;
typedef Lattice<CComplex> CoarseScalar; // used for inner products on fine field
typedef Lattice<Fobj> FineField;
LinearFunction<CoarseField> & _Poly;
OperatorFunction<FineField> & _smoother;
LinearOperatorBase<FineField> &_Linop;
RealD _coarse_relax_tol;
std::vector<FineField> &_subspace;
ImplicitlyRestartedLanczosSmoothedTester(LinearFunction<CoarseField> &Poly,
OperatorFunction<FineField> &smoother,
LinearOperatorBase<FineField> &Linop,
std::vector<FineField> &subspace,
RealD coarse_relax_tol=5.0e3)
: _smoother(smoother), _Linop(Linop), _Poly(Poly), _subspace(subspace),
_coarse_relax_tol(coarse_relax_tol)
{ };
int TestConvergence(int j,RealD eresid,CoarseField &B, RealD &eval,RealD evalMaxApprox)
{
CoarseField v(B);
RealD eval_poly = eval;
// Apply operator
_Poly(B,v);
RealD vnum = real(innerProduct(B,v)); // HermOp.
RealD vden = norm2(B);
RealD vv0 = norm2(v);
eval = vnum/vden;
v -= eval*B;
RealD vv = norm2(v) / ::pow(evalMaxApprox,2.0);
std::cout.precision(13);
std::cout<<GridLogIRL << "[" << std::setw(3)<<j<<"] "
<<"eval = "<<std::setw(25)<< eval << " (" << eval_poly << ")"
<<" |H B[i] - eval[i]B[i]|^2 / evalMaxApprox^2 " << std::setw(25) << vv
<<std::endl;
int conv=0;
if( (vv<eresid*eresid) ) conv = 1;
return conv;
}
int ReconstructEval(int j,RealD eresid,CoarseField &B, RealD &eval,RealD evalMaxApprox)
{
GridBase *FineGrid = _subspace[0].Grid();
int checkerboard = _subspace[0].Checkerboard();
FineField fB(FineGrid);fB.Checkerboard() =checkerboard;
FineField fv(FineGrid);fv.Checkerboard() =checkerboard;
blockPromote(B,fv,_subspace);
_smoother(_Linop,fv,fB);
RealD eval_poly = eval;
_Linop.HermOp(fB,fv);
RealD vnum = real(innerProduct(fB,fv)); // HermOp.
RealD vden = norm2(fB);
RealD vv0 = norm2(fv);
eval = vnum/vden;
fv -= eval*fB;
RealD vv = norm2(fv) / ::pow(evalMaxApprox,2.0);
std::cout.precision(13);
std::cout<<GridLogIRL << "[" << std::setw(3)<<j<<"] "
<<"eval = "<<std::setw(25)<< eval << " (" << eval_poly << ")"
<<" |H B[i] - eval[i]B[i]|^2 / evalMaxApprox^2 " << std::setw(25) << vv
<<std::endl;
if ( j > nbasis ) eresid = eresid*_coarse_relax_tol;
if( (vv<eresid*eresid) ) return 1;
return 0;
}
};
////////////////////////////////////////////
// Make serializable Lanczos params
////////////////////////////////////////////
template<class Fobj,class CComplex,int nbasis>
class LocalCoherenceLanczos
{
public:
typedef iVector<CComplex,nbasis > CoarseSiteVector;
typedef Lattice<CComplex> CoarseScalar; // used for inner products on fine field
typedef Lattice<CoarseSiteVector> CoarseField;
typedef Lattice<Fobj> FineField;
protected:
GridBase *_CoarseGrid;
GridBase *_FineGrid;
int _checkerboard;
LinearOperatorBase<FineField> & _FineOp;
std::vector<RealD> &evals_fine;
std::vector<RealD> &evals_coarse;
std::vector<FineField> &subspace;
std::vector<CoarseField> &evec_coarse;
private:
std::vector<RealD> _evals_fine;
std::vector<RealD> _evals_coarse;
std::vector<FineField> _subspace;
std::vector<CoarseField> _evec_coarse;
public:
LocalCoherenceLanczos(GridBase *FineGrid,
GridBase *CoarseGrid,
LinearOperatorBase<FineField> &FineOp,
int checkerboard) :
_CoarseGrid(CoarseGrid),
_FineGrid(FineGrid),
_FineOp(FineOp),
_checkerboard(checkerboard),
evals_fine (_evals_fine),
evals_coarse(_evals_coarse),
subspace (_subspace),
evec_coarse(_evec_coarse)
{
evals_fine.resize(0);
evals_coarse.resize(0);
};
//////////////////////////////////////////////////////////////////////////
// Alternate constructore, external storage for use by Hadrons module
//////////////////////////////////////////////////////////////////////////
LocalCoherenceLanczos(GridBase *FineGrid,
GridBase *CoarseGrid,
LinearOperatorBase<FineField> &FineOp,
int checkerboard,
std::vector<FineField> &ext_subspace,
std::vector<CoarseField> &ext_coarse,
std::vector<RealD> &ext_eval_fine,
std::vector<RealD> &ext_eval_coarse
) :
_CoarseGrid(CoarseGrid),
_FineGrid(FineGrid),
_FineOp(FineOp),
_checkerboard(checkerboard),
evals_fine (ext_eval_fine),
evals_coarse(ext_eval_coarse),
subspace (ext_subspace),
evec_coarse (ext_coarse)
{
evals_fine.resize(0);
evals_coarse.resize(0);
};
void Orthogonalise(void ) {
CoarseScalar InnerProd(_CoarseGrid);
std::cout << GridLogMessage <<" Gramm-Schmidt pass 1"<<std::endl;
blockOrthogonalise(InnerProd,subspace);
std::cout << GridLogMessage <<" Gramm-Schmidt pass 2"<<std::endl;
blockOrthogonalise(InnerProd,subspace);
};
template<typename T> static RealD normalise(T& v)
{
RealD nn = norm2(v);
nn = ::sqrt(nn);
v = v * (1.0/nn);
return nn;
}
/*
void fakeFine(void)
{
int Nk = nbasis;
subspace.resize(Nk,_FineGrid);
subspace[0]=1.0;
subspace[0].Checkerboard()=_checkerboard;
normalise(subspace[0]);
PlainHermOp<FineField> Op(_FineOp);
for(int k=1;k<Nk;k++){
subspace[k].Checkerboard()=_checkerboard;
Op(subspace[k-1],subspace[k]);
normalise(subspace[k]);
}
}
*/
void testFine(RealD resid)
{
assert(evals_fine.size() == nbasis);
assert(subspace.size() == nbasis);
PlainHermOp<FineField> Op(_FineOp);
ImplicitlyRestartedLanczosHermOpTester<FineField> SimpleTester(Op);
for(int k=0;k<nbasis;k++){
assert(SimpleTester.ReconstructEval(k,resid,subspace[k],evals_fine[k],1.0)==1);
}
}
void testCoarse(RealD resid,ChebyParams cheby_smooth,RealD relax)
{
assert(evals_fine.size() == nbasis);
assert(subspace.size() == nbasis);
//////////////////////////////////////////////////////////////////////////////////////////////////
// create a smoother and see if we can get a cheap convergence test and smooth inside the IRL
//////////////////////////////////////////////////////////////////////////////////////////////////
Chebyshev<FineField> ChebySmooth(cheby_smooth);
ProjectedFunctionHermOp<Fobj,CComplex,nbasis> ChebyOp (ChebySmooth,_FineOp,subspace);
ImplicitlyRestartedLanczosSmoothedTester<Fobj,CComplex,nbasis> ChebySmoothTester(ChebyOp,ChebySmooth,_FineOp,subspace,relax);
for(int k=0;k<evec_coarse.size();k++){
if ( k < nbasis ) {
assert(ChebySmoothTester.ReconstructEval(k,resid,evec_coarse[k],evals_coarse[k],1.0)==1);
} else {
assert(ChebySmoothTester.ReconstructEval(k,resid*relax,evec_coarse[k],evals_coarse[k],1.0)==1);
}
}
}
void calcFine(ChebyParams cheby_parms,int Nstop,int Nk,int Nm,RealD resid,
RealD MaxIt, RealD betastp, int MinRes)
{
assert(nbasis<=Nm);
Chebyshev<FineField> Cheby(cheby_parms);
FunctionHermOp<FineField> ChebyOp(Cheby,_FineOp);
PlainHermOp<FineField> Op(_FineOp);
evals_fine.resize(Nm);
subspace.resize(Nm,_FineGrid);
ImplicitlyRestartedLanczos<FineField> IRL(ChebyOp,Op,Nstop,Nk,Nm,resid,MaxIt,betastp,MinRes);
FineField src(_FineGrid); src=1.0; src.Checkerboard() = _checkerboard;
int Nconv;
IRL.calc(evals_fine,subspace,src,Nconv,false);
// Shrink down to number saved
assert(Nstop>=nbasis);
assert(Nconv>=nbasis);
evals_fine.resize(nbasis);
subspace.resize(nbasis,_FineGrid);
}
void calcCoarse(ChebyParams cheby_op,ChebyParams cheby_smooth,RealD relax,
int Nstop, int Nk, int Nm,RealD resid,
RealD MaxIt, RealD betastp, int MinRes)
{
Chebyshev<FineField> Cheby(cheby_op);
ProjectedHermOp<Fobj,CComplex,nbasis> Op(_FineOp,subspace);
ProjectedFunctionHermOp<Fobj,CComplex,nbasis> ChebyOp (Cheby,_FineOp,subspace);
//////////////////////////////////////////////////////////////////////////////////////////////////
// create a smoother and see if we can get a cheap convergence test and smooth inside the IRL
//////////////////////////////////////////////////////////////////////////////////////////////////
Chebyshev<FineField> ChebySmooth(cheby_smooth);
ImplicitlyRestartedLanczosSmoothedTester<Fobj,CComplex,nbasis> ChebySmoothTester(ChebyOp,ChebySmooth,_FineOp,subspace,relax);
evals_coarse.resize(Nm);
evec_coarse.resize(Nm,_CoarseGrid);
CoarseField src(_CoarseGrid); src=1.0;
ImplicitlyRestartedLanczos<CoarseField> IRL(ChebyOp,ChebyOp,ChebySmoothTester,Nstop,Nk,Nm,resid,MaxIt,betastp,MinRes);
int Nconv=0;
IRL.calc(evals_coarse,evec_coarse,src,Nconv,false);
assert(Nconv>=Nstop);
evals_coarse.resize(Nstop);
evec_coarse.resize (Nstop,_CoarseGrid);
for (int i=0;i<Nstop;i++){
std::cout << i << " Coarse eval = " << evals_coarse[i] << std::endl;
}
}
};
NAMESPACE_END(Grid);
#endif

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@ -0,0 +1,60 @@
/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/NormalEquations.h
Copyright (C) 2015
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_NORMAL_EQUATIONS_H
#define GRID_NORMAL_EQUATIONS_H
NAMESPACE_BEGIN(Grid);
///////////////////////////////////////////////////////////////////////////////////////////////////////
// Take a matrix and form an NE solver calling a Herm solver
///////////////////////////////////////////////////////////////////////////////////////////////////////
template<class Field> class NormalEquations : public OperatorFunction<Field>{
private:
SparseMatrixBase<Field> & _Matrix;
OperatorFunction<Field> & _HermitianSolver;
public:
/////////////////////////////////////////////////////
// Wrap the usual normal equations trick
/////////////////////////////////////////////////////
NormalEquations(SparseMatrixBase<Field> &Matrix, OperatorFunction<Field> &HermitianSolver)
: _Matrix(Matrix), _HermitianSolver(HermitianSolver) {};
void operator() (const Field &in, Field &out){
Field src(in.Grid());
_Matrix.Mdag(in,src);
_HermitianSolver(src,out); // Mdag M out = Mdag in
}
};
NAMESPACE_END(Grid);
#endif

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@ -0,0 +1,119 @@
/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/PrecConjugateResidual.h
Copyright (C) 2015
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_PREC_CONJUGATE_RESIDUAL_H
#define GRID_PREC_CONJUGATE_RESIDUAL_H
NAMESPACE_BEGIN(Grid);
/////////////////////////////////////////////////////////////
// Base classes for iterative processes based on operators
// single input vec, single output vec.
/////////////////////////////////////////////////////////////
template<class Field>
class PrecConjugateResidual : public OperatorFunction<Field> {
public:
RealD Tolerance;
Integer MaxIterations;
int verbose;
LinearFunction<Field> &Preconditioner;
PrecConjugateResidual(RealD tol,Integer maxit,LinearFunction<Field> &Prec) : Tolerance(tol), MaxIterations(maxit), Preconditioner(Prec)
{
verbose=1;
};
void operator() (LinearOperatorBase<Field> &Linop,const Field &src, Field &psi){
RealD a, b, c, d;
RealD cp, ssq,rsq;
RealD rAr, rAAr, rArp;
RealD pAp, pAAp;
GridBase *grid = src.Grid();
Field r(grid), p(grid), Ap(grid), Ar(grid), z(grid);
psi=zero;
r = src;
Preconditioner(r,p);
Linop.HermOpAndNorm(p,Ap,pAp,pAAp);
Ar=Ap;
rAr=pAp;
rAAr=pAAp;
cp =norm2(r);
ssq=norm2(src);
rsq=Tolerance*Tolerance*ssq;
if (verbose) std::cout<<GridLogMessage<<"PrecConjugateResidual: iteration " <<0<<" residual "<<cp<< " target"<< rsq<<std::endl;
for(int k=0;k<MaxIterations;k++){
Preconditioner(Ap,z);
RealD rq= real(innerProduct(Ap,z));
a = rAr/rq;
axpy(psi,a,p,psi);
cp = axpy_norm(r,-a,z,r);
rArp=rAr;
Linop.HermOpAndNorm(r,Ar,rAr,rAAr);
b =rAr/rArp;
axpy(p,b,p,r);
pAAp=axpy_norm(Ap,b,Ap,Ar);
if(verbose) std::cout<<GridLogMessage<<"PrecConjugateResidual: iteration " <<k<<" residual "<<cp<< " target"<< rsq<<std::endl;
if(cp<rsq) {
Linop.HermOp(psi,Ap);
axpy(r,-1.0,src,Ap);
RealD true_resid = norm2(r)/ssq;
std::cout<<GridLogMessage<<"PrecConjugateResidual: Converged on iteration " <<k
<< " computed residual "<<sqrt(cp/ssq)
<< " true residual "<<sqrt(true_resid)
<< " target " <<Tolerance <<std::endl;
return;
}
}
std::cout<<GridLogMessage<<"PrecConjugateResidual did NOT converge"<<std::endl;
assert(0);
}
};
NAMESPACE_END(Grid);
#endif

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@ -0,0 +1,230 @@
/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/PrecGeneralisedConjugateResidual.h
Copyright (C) 2015
Author: Azusa Yamaguchi <ayamaguc@staffmail.ed.ac.uk>
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_PREC_GCR_H
#define GRID_PREC_GCR_H
///////////////////////////////////////////////////////////////////////////////////////////////////////
//VPGCR Abe and Zhang, 2005.
//INTERNATIONAL JOURNAL OF NUMERICAL ANALYSIS AND MODELING
//Computing and Information Volume 2, Number 2, Pages 147-161
//NB. Likely not original reference since they are focussing on a preconditioner variant.
// but VPGCR was nicely written up in their paper
///////////////////////////////////////////////////////////////////////////////////////////////////////
NAMESPACE_BEGIN(Grid);
template<class Field>
class PrecGeneralisedConjugateResidual : public OperatorFunction<Field> {
public:
RealD Tolerance;
Integer MaxIterations;
int verbose;
int mmax;
int nstep;
int steps;
GridStopWatch PrecTimer;
GridStopWatch MatTimer;
GridStopWatch LinalgTimer;
LinearFunction<Field> &Preconditioner;
PrecGeneralisedConjugateResidual(RealD tol,Integer maxit,LinearFunction<Field> &Prec,int _mmax,int _nstep) :
Tolerance(tol),
MaxIterations(maxit),
Preconditioner(Prec),
mmax(_mmax),
nstep(_nstep)
{
verbose=1;
};
void operator() (LinearOperatorBase<Field> &Linop,const Field &src, Field &psi){
psi=Zero();
RealD cp, ssq,rsq;
ssq=norm2(src);
rsq=Tolerance*Tolerance*ssq;
Field r(src.Grid());
PrecTimer.Reset();
MatTimer.Reset();
LinalgTimer.Reset();
GridStopWatch SolverTimer;
SolverTimer.Start();
steps=0;
for(int k=0;k<MaxIterations;k++){
cp=GCRnStep(Linop,src,psi,rsq);
std::cout<<GridLogMessage<<"VPGCR("<<mmax<<","<<nstep<<") "<< steps <<" steps cp = "<<cp<<std::endl;
if(cp<rsq) {
SolverTimer.Stop();
Linop.HermOp(psi,r);
axpy(r,-1.0,src,r);
RealD tr = norm2(r);
std::cout<<GridLogMessage<<"PrecGeneralisedConjugateResidual: Converged on iteration " <<steps
<< " computed residual "<<sqrt(cp/ssq)
<< " true residual " <<sqrt(tr/ssq)
<< " target " <<Tolerance <<std::endl;
std::cout<<GridLogMessage<<"VPGCR Time elapsed: Total "<< SolverTimer.Elapsed() <<std::endl;
std::cout<<GridLogMessage<<"VPGCR Time elapsed: Precon "<< PrecTimer.Elapsed() <<std::endl;
std::cout<<GridLogMessage<<"VPGCR Time elapsed: Matrix "<< MatTimer.Elapsed() <<std::endl;
std::cout<<GridLogMessage<<"VPGCR Time elapsed: Linalg "<< LinalgTimer.Elapsed() <<std::endl;
return;
}
}
std::cout<<GridLogMessage<<"Variable Preconditioned GCR did not converge"<<std::endl;
assert(0);
}
RealD GCRnStep(LinearOperatorBase<Field> &Linop,const Field &src, Field &psi,RealD rsq){
RealD cp;
RealD a, b;
RealD zAz, zAAz;
RealD rq;
GridBase *grid = src.Grid();
Field r(grid);
Field z(grid);
Field tmp(grid);
Field ttmp(grid);
Field Az(grid);
////////////////////////////////
// history for flexible orthog
////////////////////////////////
std::vector<Field> q(mmax,grid);
std::vector<Field> p(mmax,grid);
std::vector<RealD> qq(mmax);
//////////////////////////////////
// initial guess x0 is taken as nonzero.
// r0=src-A x0 = src
//////////////////////////////////
MatTimer.Start();
Linop.HermOpAndNorm(psi,Az,zAz,zAAz);
MatTimer.Stop();
r=src-Az;
/////////////////////
// p = Prec(r)
/////////////////////
PrecTimer.Start();
Preconditioner(r,z);
PrecTimer.Stop();
MatTimer.Start();
Linop.HermOp(z,tmp);
MatTimer.Stop();
ttmp=tmp;
tmp=tmp-r;
/*
std::cout<<GridLogMessage<<r<<std::endl;
std::cout<<GridLogMessage<<z<<std::endl;
std::cout<<GridLogMessage<<ttmp<<std::endl;
std::cout<<GridLogMessage<<tmp<<std::endl;
*/
MatTimer.Start();
Linop.HermOpAndNorm(z,Az,zAz,zAAz);
MatTimer.Stop();
//p[0],q[0],qq[0]
p[0]= z;
q[0]= Az;
qq[0]= zAAz;
cp =norm2(r);
for(int k=0;k<nstep;k++){
steps++;
int kp = k+1;
int peri_k = k %mmax;
int peri_kp= kp%mmax;
rq= real(innerProduct(r,q[peri_k])); // what if rAr not real?
a = rq/qq[peri_k];
axpy(psi,a,p[peri_k],psi);
cp = axpy_norm(r,-a,q[peri_k],r);
if((k==nstep-1)||(cp<rsq)){
return cp;
}
std::cout<<GridLogMessage<< " VPGCR_step["<<steps<<"] resid " <<sqrt(cp/rsq)<<std::endl;
PrecTimer.Start();
Preconditioner(r,z);// solve Az = r
PrecTimer.Stop();
MatTimer.Start();
Linop.HermOpAndNorm(z,Az,zAz,zAAz);
Linop.HermOp(z,tmp);
MatTimer.Stop();
tmp=tmp-r;
std::cout<<GridLogMessage<< " Preconditioner resid " <<sqrt(norm2(tmp)/norm2(r))<<std::endl;
q[peri_kp]=Az;
p[peri_kp]=z;
int northog = ((kp)>(mmax-1))?(mmax-1):(kp); // if more than mmax done, we orthog all mmax history.
for(int back=0;back<northog;back++){
int peri_back=(k-back)%mmax; assert((k-back)>=0);
b=-real(innerProduct(q[peri_back],Az))/qq[peri_back];
p[peri_kp]=p[peri_kp]+b*p[peri_back];
q[peri_kp]=q[peri_kp]+b*q[peri_back];
}
qq[peri_kp]=norm2(q[peri_kp]); // could use axpy_norm
}
assert(0); // never reached
return cp;
}
};
NAMESPACE_END(Grid);
#endif

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@ -0,0 +1,473 @@
/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/SchurRedBlack.h
Copyright (C) 2015
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution directory
*************************************************************************************/
/* END LEGAL */
#ifndef GRID_SCHUR_RED_BLACK_H
#define GRID_SCHUR_RED_BLACK_H
/*
* Red black Schur decomposition
*
* M = (Mee Meo) = (1 0 ) (Mee 0 ) (1 Mee^{-1} Meo)
* (Moe Moo) (Moe Mee^-1 1 ) (0 Moo-Moe Mee^-1 Meo) (0 1 )
* = L D U
*
* L^-1 = (1 0 )
* (-MoeMee^{-1} 1 )
* L^{dag} = ( 1 Mee^{-dag} Moe^{dag} )
* ( 0 1 )
* L^{-d} = ( 1 -Mee^{-dag} Moe^{dag} )
* ( 0 1 )
*
* U^-1 = (1 -Mee^{-1} Meo)
* (0 1 )
* U^{dag} = ( 1 0)
* (Meo^dag Mee^{-dag} 1)
* U^{-dag} = ( 1 0)
* (-Meo^dag Mee^{-dag} 1)
***********************
* M psi = eta
***********************
*Odd
* i) D_oo psi_o = L^{-1} eta_o
* eta_o' = (D_oo)^dag (eta_o - Moe Mee^{-1} eta_e)
*
* Wilson:
* (D_oo)^{\dag} D_oo psi_o = (D_oo)^dag L^{-1} eta_o
* Stag:
* D_oo psi_o = L^{-1} eta = (eta_o - Moe Mee^{-1} eta_e)
*
* L^-1 eta_o= (1 0 ) (e
* (-MoeMee^{-1} 1 )
*
*Even
* ii) Mee psi_e + Meo psi_o = src_e
*
* => sol_e = M_ee^-1 * ( src_e - Meo sol_o )...
*
*
* TODO: Other options:
*
* a) change checkerboards for Schur e<->o
*
* Left precon by Moo^-1
* b) Doo^{dag} M_oo^-dag Moo^-1 Doo psi_0 = (D_oo)^dag M_oo^-dag Moo^-1 L^{-1} eta_o
* eta_o' = (D_oo)^dag M_oo^-dag Moo^-1 (eta_o - Moe Mee^{-1} eta_e)
*
* Right precon by Moo^-1
* c) M_oo^-dag Doo^{dag} Doo Moo^-1 phi_0 = M_oo^-dag (D_oo)^dag L^{-1} eta_o
* eta_o' = M_oo^-dag (D_oo)^dag (eta_o - Moe Mee^{-1} eta_e)
* psi_o = M_oo^-1 phi_o
* TODO: Deflation
*/
namespace Grid {
///////////////////////////////////////////////////////////////////////////////////////////////////////
// Use base class to share code
///////////////////////////////////////////////////////////////////////////////////////////////////////
///////////////////////////////////////////////////////////////////////////////////////////////////////
// Take a matrix and form a Red Black solver calling a Herm solver
// Use of RB info prevents making SchurRedBlackSolve conform to standard interface
///////////////////////////////////////////////////////////////////////////////////////////////////////
template<class Field> class SchurRedBlackBase {
protected:
typedef CheckerBoardedSparseMatrixBase<Field> Matrix;
OperatorFunction<Field> & _HermitianRBSolver;
int CBfactorise;
bool subGuess;
public:
SchurRedBlackBase(OperatorFunction<Field> &HermitianRBSolver, const bool initSubGuess = false) :
_HermitianRBSolver(HermitianRBSolver)
{
CBfactorise = 0;
subtractGuess(initSubGuess);
};
void subtractGuess(const bool initSubGuess)
{
subGuess = initSubGuess;
}
bool isSubtractGuess(void)
{
return subGuess;
}
/////////////////////////////////////////////////////////////
// Shared code
/////////////////////////////////////////////////////////////
void operator() (Matrix & _Matrix,const Field &in, Field &out){
ZeroGuesser<Field> guess;
(*this)(_Matrix,in,out,guess);
}
void operator()(Matrix &_Matrix, const std::vector<Field> &in, std::vector<Field> &out)
{
ZeroGuesser<Field> guess;
(*this)(_Matrix,in,out,guess);
}
template<class Guesser>
void operator()(Matrix &_Matrix, const std::vector<Field> &in, std::vector<Field> &out,Guesser &guess)
{
GridBase *grid = _Matrix.RedBlackGrid();
GridBase *fgrid= _Matrix.Grid();
int nblock = in.size();
std::vector<Field> src_o(nblock,grid);
std::vector<Field> sol_o(nblock,grid);
std::vector<Field> guess_save;
Field resid(fgrid);
Field tmp(grid);
////////////////////////////////////////////////
// Prepare RedBlack source
////////////////////////////////////////////////
for(int b=0;b<nblock;b++){
RedBlackSource(_Matrix,in[b],tmp,src_o[b]);
}
////////////////////////////////////////////////
// Make the guesses
////////////////////////////////////////////////
if ( subGuess ) guess_save.resize(nblock,grid);
for(int b=0;b<nblock;b++){
guess(src_o[b],sol_o[b]);
if ( subGuess ) {
guess_save[b] = sol_o[b];
}
}
//////////////////////////////////////////////////////////////
// Call the block solver
//////////////////////////////////////////////////////////////
std::cout<<GridLogMessage << "SchurRedBlackBase calling the solver for "<<nblock<<" RHS" <<std::endl;
RedBlackSolve(_Matrix,src_o,sol_o);
////////////////////////////////////////////////
// A2A boolean behavioural control & reconstruct other checkerboard
////////////////////////////////////////////////
for(int b=0;b<nblock;b++) {
if (subGuess) sol_o[b] = sol_o[b] - guess_save[b];
///////// Needs even source //////////////
pickCheckerboard(Even,tmp,in[b]);
RedBlackSolution(_Matrix,sol_o[b],tmp,out[b]);
/////////////////////////////////////////////////
// Check unprec residual if possible
/////////////////////////////////////////////////
if ( ! subGuess ) {
_Matrix.M(out[b],resid);
resid = resid-in[b];
RealD ns = norm2(in[b]);
RealD nr = norm2(resid);
std::cout<<GridLogMessage<< "SchurRedBlackBase solver true unprec resid["<<b<<"] "<<std::sqrt(nr/ns) << std::endl;
} else {
std::cout<<GridLogMessage<< "SchurRedBlackBase Guess subtracted after solve["<<b<<"] " << std::endl;
}
}
}
template<class Guesser>
void operator() (Matrix & _Matrix,const Field &in, Field &out,Guesser &guess){
// FIXME CGdiagonalMee not implemented virtual function
// FIXME use CBfactorise to control schur decomp
GridBase *grid = _Matrix.RedBlackGrid();
GridBase *fgrid= _Matrix.Grid();
Field resid(fgrid);
Field src_o(grid);
Field src_e(grid);
Field sol_o(grid);
////////////////////////////////////////////////
// RedBlack source
////////////////////////////////////////////////
RedBlackSource(_Matrix,in,src_e,src_o);
////////////////////////////////
// Construct the guess
////////////////////////////////
Field tmp(grid);
guess(src_o,sol_o);
Field guess_save(grid);
guess_save = sol_o;
//////////////////////////////////////////////////////////////
// Call the red-black solver
//////////////////////////////////////////////////////////////
RedBlackSolve(_Matrix,src_o,sol_o);
////////////////////////////////////////////////
// Fionn A2A boolean behavioural control
////////////////////////////////////////////////
if (subGuess) sol_o= sol_o-guess_save;
///////////////////////////////////////////////////
// RedBlack solution needs the even source
///////////////////////////////////////////////////
RedBlackSolution(_Matrix,sol_o,src_e,out);
// Verify the unprec residual
if ( ! subGuess ) {
_Matrix.M(out,resid);
resid = resid-in;
RealD ns = norm2(in);
RealD nr = norm2(resid);
std::cout<<GridLogMessage << "SchurRedBlackBase solver true unprec resid "<< std::sqrt(nr/ns) << std::endl;
} else {
std::cout << GridLogMessage << "SchurRedBlackBase Guess subtracted after solve." << std::endl;
}
}
/////////////////////////////////////////////////////////////
// Override in derived. Not virtual as template methods
/////////////////////////////////////////////////////////////
virtual void RedBlackSource (Matrix & _Matrix,const Field &src, Field &src_e,Field &src_o) =0;
virtual void RedBlackSolution(Matrix & _Matrix,const Field &sol_o, const Field &src_e,Field &sol) =0;
virtual void RedBlackSolve (Matrix & _Matrix,const Field &src_o, Field &sol_o) =0;
virtual void RedBlackSolve (Matrix & _Matrix,const std::vector<Field> &src_o, std::vector<Field> &sol_o)=0;
};
template<class Field> class SchurRedBlackStaggeredSolve : public SchurRedBlackBase<Field> {
public:
typedef CheckerBoardedSparseMatrixBase<Field> Matrix;
SchurRedBlackStaggeredSolve(OperatorFunction<Field> &HermitianRBSolver, const bool initSubGuess = false)
: SchurRedBlackBase<Field> (HermitianRBSolver,initSubGuess)
{
}
//////////////////////////////////////////////////////
// Override RedBlack specialisation
//////////////////////////////////////////////////////
virtual void RedBlackSource(Matrix & _Matrix,const Field &src, Field &src_e,Field &src_o)
{
GridBase *grid = _Matrix.RedBlackGrid();
GridBase *fgrid= _Matrix.Grid();
Field tmp(grid);
Field Mtmp(grid);
pickCheckerboard(Even,src_e,src);
pickCheckerboard(Odd ,src_o,src);
/////////////////////////////////////////////////////
// src_o = (source_o - Moe MeeInv source_e)
/////////////////////////////////////////////////////
_Matrix.MooeeInv(src_e,tmp); assert( tmp.Checkerboard() ==Even);
_Matrix.Meooe (tmp,Mtmp); assert( Mtmp.Checkerboard() ==Odd);
tmp=src_o-Mtmp; assert( tmp.Checkerboard() ==Odd);
_Matrix.Mooee(tmp,src_o); // Extra factor of "m" in source from dumb choice of matrix norm.
}
virtual void RedBlackSolution(Matrix & _Matrix,const Field &sol_o, const Field &src_e_c,Field &sol)
{
GridBase *grid = _Matrix.RedBlackGrid();
GridBase *fgrid= _Matrix.Grid();
Field tmp(grid);
Field sol_e(grid);
Field src_e(grid);
src_e = src_e_c; // Const correctness
///////////////////////////////////////////////////
// sol_e = M_ee^-1 * ( src_e - Meo sol_o )...
///////////////////////////////////////////////////
_Matrix.Meooe(sol_o,tmp); assert( tmp.Checkerboard() ==Even);
src_e = src_e-tmp; assert( src_e.Checkerboard() ==Even);
_Matrix.MooeeInv(src_e,sol_e); assert( sol_e.Checkerboard() ==Even);
setCheckerboard(sol,sol_e); assert( sol_e.Checkerboard() ==Even);
setCheckerboard(sol,sol_o); assert( sol_o.Checkerboard() ==Odd );
}
virtual void RedBlackSolve (Matrix & _Matrix,const Field &src_o, Field &sol_o)
{
SchurStaggeredOperator<Matrix,Field> _HermOpEO(_Matrix);
this->_HermitianRBSolver(_HermOpEO,src_o,sol_o); assert(sol_o.Checkerboard()==Odd);
};
virtual void RedBlackSolve (Matrix & _Matrix,const std::vector<Field> &src_o, std::vector<Field> &sol_o)
{
SchurStaggeredOperator<Matrix,Field> _HermOpEO(_Matrix);
this->_HermitianRBSolver(_HermOpEO,src_o,sol_o);
}
};
template<class Field> using SchurRedBlackStagSolve = SchurRedBlackStaggeredSolve<Field>;
///////////////////////////////////////////////////////////////////////////////////////////////////////
// Site diagonal has Mooee on it.
///////////////////////////////////////////////////////////////////////////////////////////////////////
template<class Field> class SchurRedBlackDiagMooeeSolve : public SchurRedBlackBase<Field> {
public:
typedef CheckerBoardedSparseMatrixBase<Field> Matrix;
SchurRedBlackDiagMooeeSolve(OperatorFunction<Field> &HermitianRBSolver, const bool initSubGuess = false)
: SchurRedBlackBase<Field> (HermitianRBSolver,initSubGuess) {};
//////////////////////////////////////////////////////
// Override RedBlack specialisation
//////////////////////////////////////////////////////
virtual void RedBlackSource(Matrix & _Matrix,const Field &src, Field &src_e,Field &src_o)
{
GridBase *grid = _Matrix.RedBlackGrid();
GridBase *fgrid= _Matrix.Grid();
Field tmp(grid);
Field Mtmp(grid);
pickCheckerboard(Even,src_e,src);
pickCheckerboard(Odd ,src_o,src);
/////////////////////////////////////////////////////
// src_o = Mdag * (source_o - Moe MeeInv source_e)
/////////////////////////////////////////////////////
_Matrix.MooeeInv(src_e,tmp); assert( tmp.Checkerboard() ==Even);
_Matrix.Meooe (tmp,Mtmp); assert( Mtmp.Checkerboard() ==Odd);
tmp=src_o-Mtmp; assert( tmp.Checkerboard() ==Odd);
// get the right MpcDag
SchurDiagMooeeOperator<Matrix,Field> _HermOpEO(_Matrix);
_HermOpEO.MpcDag(tmp,src_o); assert(src_o.Checkerboard() ==Odd);
}
virtual void RedBlackSolution(Matrix & _Matrix,const Field &sol_o, const Field &src_e,Field &sol)
{
GridBase *grid = _Matrix.RedBlackGrid();
GridBase *fgrid= _Matrix.Grid();
Field tmp(grid);
Field sol_e(grid);
Field src_e_i(grid);
///////////////////////////////////////////////////
// sol_e = M_ee^-1 * ( src_e - Meo sol_o )...
///////////////////////////////////////////////////
_Matrix.Meooe(sol_o,tmp); assert( tmp.Checkerboard() ==Even);
src_e_i = src_e-tmp; assert( src_e_i.Checkerboard() ==Even);
_Matrix.MooeeInv(src_e_i,sol_e); assert( sol_e.Checkerboard() ==Even);
setCheckerboard(sol,sol_e); assert( sol_e.Checkerboard() ==Even);
setCheckerboard(sol,sol_o); assert( sol_o.Checkerboard() ==Odd );
}
virtual void RedBlackSolve (Matrix & _Matrix,const Field &src_o, Field &sol_o)
{
SchurDiagMooeeOperator<Matrix,Field> _HermOpEO(_Matrix);
this->_HermitianRBSolver(_HermOpEO,src_o,sol_o); assert(sol_o.Checkerboard()==Odd);
};
virtual void RedBlackSolve (Matrix & _Matrix,const std::vector<Field> &src_o, std::vector<Field> &sol_o)
{
SchurDiagMooeeOperator<Matrix,Field> _HermOpEO(_Matrix);
this->_HermitianRBSolver(_HermOpEO,src_o,sol_o);
}
};
///////////////////////////////////////////////////////////////////////////////////////////////////////
// Site diagonal is identity, right preconditioned by Mee^inv
// ( 1 - Meo Moo^inv Moe Mee^inv ) phi =( 1 - Meo Moo^inv Moe Mee^inv ) Mee psi = = eta = eta
//=> psi = MeeInv phi
///////////////////////////////////////////////////////////////////////////////////////////////////////
template<class Field> class SchurRedBlackDiagTwoSolve : public SchurRedBlackBase<Field> {
public:
typedef CheckerBoardedSparseMatrixBase<Field> Matrix;
/////////////////////////////////////////////////////
// Wrap the usual normal equations Schur trick
/////////////////////////////////////////////////////
SchurRedBlackDiagTwoSolve(OperatorFunction<Field> &HermitianRBSolver, const bool initSubGuess = false)
: SchurRedBlackBase<Field>(HermitianRBSolver,initSubGuess) {};
virtual void RedBlackSource(Matrix & _Matrix,const Field &src, Field &src_e,Field &src_o)
{
GridBase *grid = _Matrix.RedBlackGrid();
GridBase *fgrid= _Matrix.Grid();
SchurDiagTwoOperator<Matrix,Field> _HermOpEO(_Matrix);
Field tmp(grid);
Field Mtmp(grid);
pickCheckerboard(Even,src_e,src);
pickCheckerboard(Odd ,src_o,src);
/////////////////////////////////////////////////////
// src_o = Mdag * (source_o - Moe MeeInv source_e)
/////////////////////////////////////////////////////
_Matrix.MooeeInv(src_e,tmp); assert( tmp.Checkerboard() ==Even);
_Matrix.Meooe (tmp,Mtmp); assert( Mtmp.Checkerboard() ==Odd);
tmp=src_o-Mtmp; assert( tmp.Checkerboard() ==Odd);
// get the right MpcDag
_HermOpEO.MpcDag(tmp,src_o); assert(src_o.Checkerboard() ==Odd);
}
virtual void RedBlackSolution(Matrix & _Matrix,const Field &sol_o, const Field &src_e,Field &sol)
{
GridBase *grid = _Matrix.RedBlackGrid();
GridBase *fgrid= _Matrix.Grid();
Field sol_o_i(grid);
Field tmp(grid);
Field sol_e(grid);
////////////////////////////////////////////////
// MooeeInv due to pecond
////////////////////////////////////////////////
_Matrix.MooeeInv(sol_o,tmp);
sol_o_i = tmp;
///////////////////////////////////////////////////
// sol_e = M_ee^-1 * ( src_e - Meo sol_o )...
///////////////////////////////////////////////////
_Matrix.Meooe(sol_o_i,tmp); assert( tmp.Checkerboard() ==Even);
tmp = src_e-tmp; assert( src_e.Checkerboard() ==Even);
_Matrix.MooeeInv(tmp,sol_e); assert( sol_e.Checkerboard() ==Even);
setCheckerboard(sol,sol_e); assert( sol_e.Checkerboard() ==Even);
setCheckerboard(sol,sol_o_i); assert( sol_o_i.Checkerboard() ==Odd );
};
virtual void RedBlackSolve (Matrix & _Matrix,const Field &src_o, Field &sol_o)
{
SchurDiagTwoOperator<Matrix,Field> _HermOpEO(_Matrix);
this->_HermitianRBSolver(_HermOpEO,src_o,sol_o);
};
virtual void RedBlackSolve (Matrix & _Matrix,const std::vector<Field> &src_o, std::vector<Field> &sol_o)
{
SchurDiagTwoOperator<Matrix,Field> _HermOpEO(_Matrix);
this->_HermitianRBSolver(_HermOpEO,src_o,sol_o);
}
};
}
#endif