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Grid/lib/algorithms/iterative/ImplicitlyRestartedLanczos.h

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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 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 typename Field::vector_object vobj;
GridBase* grid = basis[0]._grid;
parallel_region
{
std::vector < vobj > B(Nm); // Thread private
parallel_for_internal(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[k]._odata[ss];
}
}
for(int j=j0; j<j1; ++j){
basis[j]._odata[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;
parallel_for(int ss=0;ss < grid->oSites();ss++){
vobj B = zero;
for(int k=k0; k<k1; ++k){
B +=Qt(j,k) * basis[k]._odata[ss];
}
result._odata[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);
std::swap(_v[i]._odata,_v[idx[i]]._odata); // 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 = 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"<<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 = 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.
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) ) {
if ( j > Nconv ) {
Nconv=j+1;
jj=Nstop; // Terminate the scan
}
}
}
// Do evec[0] for good measure
{
int j=0;
RealD e = eval2_copy[0];
basisRotateJ(B,evec,Qt,j,0,Nk,Nm);
_Tester.TestConvergence(j,eresid,B,e,evalMaxApprox);
}
// 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:=Avk−βkv_{k−1}
4. αk:=(wk,vk) //
5. wk:=wk−αkvk // 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:=wk−αkvk
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 = 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();
}
};
}
#endif
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