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Grid/Grid/algorithms/iterative/ImplicitlyRestartedBlockLanczos.h
Yong-Chull Jang b89b1280d5 use gemm twice to complete the Gram Schmidt
2020-03-31 05:39:31 -04:00

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/*************************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: ./lib/algorithms/iterative/ImplicitlyRestartedBlockLanczos.h
Copyright (C) 2015
Author: Peter Boyle <paboyle@ph.ed.ac.uk>
Author: Chulwoo Jung
Author: Yong-Chull Jang <ypj@quark.phy.bnl.gov>
Author: Guido Cossu
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_IRBL_H
#define GRID_IRBL_H
#include <string.h> //memset
#ifdef USE_LAPACK
#include <mkl_lapack.h>
#endif
#undef USE_LAPACK
#define Glog std::cout << GridLogMessage
#ifdef GRID_NVCC
#include "cublas_v2.h"
#endif
namespace Grid {
////////////////////////////////////////////////////////////////////////////////
// Helper class for sorting the evalues AND evectors by Field
// Use pointer swizzle on vectors SHOULD GET RID OF IT SOON!
////////////////////////////////////////////////////////////////////////////////
template<class Field>
class SortEigen {
private:
static bool less_lmd(RealD left,RealD right){
return left > right;
}
static bool less_pair(std::pair<RealD,Field const*>& left,
std::pair<RealD,Field const*>& right){
return left.first > (right.first);
}
public:
void push(std::vector<RealD>& lmd,std::vector<Field>& evec,int N) {
////////////////////////////////////////////////////////////////////////
// PAB: FIXME: VERY VERY VERY wasteful: takes a copy of the entire vector set.
// : The vector reorder should be done by pointer swizzle somehow
////////////////////////////////////////////////////////////////////////
std::vector<Field> cpy(lmd.size(),evec[0].Grid());
for(int i=0;i<lmd.size();i++) cpy[i] = evec[i];
std::vector<std::pair<RealD, Field const*> > emod(lmd.size());
for(int i=0;i<lmd.size();++i) emod[i] = std::pair<RealD,Field const*>(lmd[i],&cpy[i]);
partial_sort(emod.begin(),emod.begin()+N,emod.end(),less_pair);
typename std::vector<std::pair<RealD, Field const*> >::iterator it = emod.begin();
for(int i=0;i<N;++i){
lmd[i]=it->first;
evec[i]=*(it->second);
++it;
}
}
void push(std::vector<RealD>& lmd,int N) {
std::partial_sort(lmd.begin(),lmd.begin()+N,lmd.end(),less_lmd);
}
bool saturated(RealD lmd, RealD thrs) {
return fabs(lmd) > fabs(thrs);
}
};
enum class LanczosType { irbl, rbl };
enum IRBLdiagonalisation {
IRBLdiagonaliseWithDSTEGR,
IRBLdiagonaliseWithQR,
IRBLdiagonaliseWithEigen
};
/////////////////////////////////////////////////////////////
// Implicitly restarted block lanczos
/////////////////////////////////////////////////////////////
template<class Field>
class ImplicitlyRestartedBlockLanczos {
private:
std::string cname = std::string("ImplicitlyRestartedBlockLanczos");
int MaxIter; // Max iterations
int Nstop; // Number of evecs checked for convergence
int Nu; // Number of vecs in the unit block
int Nk; // Number of converged sought
int Nm; // total number of vectors
int Nblock_k; // Nk/Nu
int Nblock_m; // Nm/Nu
int Nconv_test_interval; // Number of skipped vectors when checking a convergence
RealD eresid;
IRBLdiagonalisation diagonalisation;
int split_test; //test split in the first iteration
////////////////////////////////////
// Embedded objects
////////////////////////////////////
SortEigen<Field> _sort;
LinearOperatorBase<Field> &_Linop;
LinearOperatorBase<Field> &_SLinop;//for split
OperatorFunction<Field> &_poly;
GridRedBlackCartesian * f_grid;
GridRedBlackCartesian * sf_grid;
int mrhs;
/////////////////////////
// BLAS objects
/////////////////////////
#ifdef GRID_NVCC
cudaError_t cudaStat;
cuDoubleComplex *w_acc, *evec_acc, *c_acc;
#endif
int Nevec_acc; // Number of eigenvectors stored in the buffer evec_acc
/////////////////////////
// Constructor
/////////////////////////
public:
ImplicitlyRestartedBlockLanczos(LinearOperatorBase<Field> &Linop, // op
LinearOperatorBase<Field> &SLinop, // op
GridRedBlackCartesian * FrbGrid,
GridRedBlackCartesian * SFrbGrid,
int _mrhs,
OperatorFunction<Field> & poly, // polynomial
int _Nstop, // really sought vecs
int _Nconv_test_interval, // conv check interval
int _Nu, // vecs in the unit block
int _Nk, // sought vecs
int _Nm, // total vecs
RealD _eresid, // resid in lmd deficit
int _MaxIter, // Max iterations
IRBLdiagonalisation _diagonalisation = IRBLdiagonaliseWithEigen)
: _Linop(Linop), _SLinop(SLinop), _poly(poly),sf_grid(SFrbGrid),f_grid(FrbGrid),
Nstop(_Nstop), Nconv_test_interval(_Nconv_test_interval), mrhs(_mrhs),
Nu(_Nu), Nk(_Nk), Nm(_Nm),
Nblock_m(_Nm/_Nu), Nblock_k(_Nk/_Nu),
//eresid(_eresid), MaxIter(10),
eresid(_eresid), MaxIter(_MaxIter),
diagonalisation(_diagonalisation),split_test(0),
Nevec_acc(_Nu)
{ assert( (Nk%Nu==0) && (Nm%Nu==0) ); };
////////////////////////////////
// Helpers
////////////////////////////////
static RealD normalize(Field& v, int if_print=0)
{
RealD nn = norm2(v);
nn = sqrt(nn);
#if 0
if(if_print && nn < 1e20)
Glog<<"normalize: "<<nn<<std::endl;
#endif
v = v * (1.0/nn);
return nn;
}
void orthogonalize(Field& w, std::vector<Field>& evec, int k, int if_print=0)
{
typedef typename Field::scalar_type MyComplex;
// MyComplex ip;
ComplexD ip;
for(int j=0; j<k; ++j){
ip = innerProduct(evec[j],w);
if(if_print)
if( norm(ip)/norm2(w) > 1e-14)
Glog<<"orthogonalize before: "<<j<<" of "<<k<<" "<< ip <<std::endl;
w = w - ip * evec[j];
if(if_print) {
ip = innerProduct(evec[j],w);
if( norm(ip)/norm2(w) > 1e-14)
Glog<<"orthogonalize after: "<<j<<" of "<<k<<" "<< ip <<std::endl;
}
}
assert(normalize(w,if_print) != 0);
}
void reorthogonalize(Field& w, std::vector<Field>& evec, int k)
{
orthogonalize(w, evec, k,1);
}
void orthogonalize(std::vector<Field>& w, int _Nu, std::vector<Field>& evec, int k, int if_print=0)
{
typedef typename Field::scalar_type MyComplex;
MyComplex ip;
// ComplexD ip;
for(int j=0; j<k; ++j){
for(int i=0; i<_Nu; ++i){
ip = innerProduct(evec[j],w[i]);
#if 0
if(if_print)
if( norm(ip)/norm2(w[i]) > 1e-14)
Glog<<"orthogonalize before: "<<i<<" "<<j<<" of "<<k<<" "<< ip <<std::endl;
#endif
w[i] = w[i] - ip * evec[j];
#if 0
if(if_print) {
ip = innerProduct(evec[j],w[i]);
if( norm(ip)/norm2(w[i]) > 1e-14)
Glog<<"orthogonalize after: "<<i<<" "<<j<<" of "<<k<<" "<< ip <<std::endl;
}
#endif
}}
for(int i=0; i<_Nu; ++i)
assert(normalize(w[i],if_print) !=0);
}
void orthogonalize_blas(std::vector<Field>& w, std::vector<Field>& evec, int R, int do_print=0)
{
#ifdef GRID_NVCC
Glog << "cuBLAS orthogonalize" << std::endl;
typedef typename Field::vector_object vobj;
typedef typename vobj::scalar_type scalar_type;
typedef typename vobj::vector_type vector_type;
typedef typename Field::scalar_type MyComplex;
GridBase *grid = w[0].Grid();
const uint64_t sites = grid->lSites();
int Nbatch = R/Nevec_acc;
assert( R%Nevec_acc == 0 );
Glog << "nBatch, Nevec_acc, R, Nu = "
<< Nbatch << "," << Nevec_acc << "," << R << "," << Nu << std::endl;
#if 0 // a trivial test
for (int col=0; col<Nu; ++col) {
for (size_t row=0; row<sites*12; ++row) {
w_acc[col*sites*12+row].x = 1.0;
w_acc[col*sites*12+row].y = 0.0;
}
}
#else
for (int col=0; col<Nu; ++col) {
auto w_v = w[col].View();
cuDoubleComplex *z = reinterpret_cast<cuDoubleComplex*>(&w_v[0]);
for (size_t row=0; row<sites*12; ++row) {
w_acc[col*sites*12+row] = z[row];
}
}
#endif
cublasHandle_t handle;
cublasStatus_t stat;
stat = cublasCreate(&handle);
Glog << "cuBLAS Zgemm"<< std::endl;
for (int b=0; b<Nbatch; ++b) {
#if 0 // a trivial test
for (int col=0; col<Nevec_acc; ++col) {
for (size_t row=0; row<sites*12; ++row) {
evec_acc[col*sites*12+row].x = 1.0;
evec_acc[col*sites*12+row].y = 0.0;
}
}
#else
for (int col=0; col<Nevec_acc; ++col) {
auto evec_v = evec[b*Nevec_acc+col].View();
cuDoubleComplex *z = reinterpret_cast<cuDoubleComplex*>(&evec_v[0]);
for (size_t row=0; row<sites*12; ++row) {
evec_acc[col*sites*12+row] = z[row];
}
}
#endif
cuDoubleComplex alpha = make_cuDoubleComplex(1.0,0.0);
cuDoubleComplex beta = make_cuDoubleComplex(0.0,0.0);
stat = cublasZgemm(handle, CUBLAS_OP_C, CUBLAS_OP_N, Nevec_acc, Nu, 12*sites,
&alpha,
evec_acc, 12*sites, w_acc, 12*sites,
&beta,
c_acc, Nevec_acc);
//Glog << stat << std::endl;
grid->GlobalSumVector((double*)c_acc,2*Nu*Nevec_acc);
#if 0
for (int i=0; i<Nu; ++i) {
for (size_t j=0; j<Nevec_acc; ++j) {
cuDoubleComplex z = c_acc[i*Nevec_acc+j];
MyComplex ip(z.x,z.y);
if (do_print) {
Glog << "<evec,w>[" << j << "," << i << "] = "
<< z.x << " + i " << z.y << std::endl;
}
w[i] = w[i] - ip * evec[b*Nevec_acc+j];
}
}
#else
alpha = make_cuDoubleComplex(-1.0,0.0);
beta = make_cuDoubleComplex(1.0,0.0);
stat = cublasZgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, 12*sites, Nu, Nevec_acc,
&alpha,
evec_acc, 12*sites, c_acc, Nevec_acc,
&beta,
w_acc, 12*sites);
//Glog << stat << std::endl;
#endif
}
#if 1
for (int col=0; col<Nu; ++col) {
auto w_v = w[col].View();
cuDoubleComplex *z = reinterpret_cast<cuDoubleComplex*>(&w_v[0]);
for (size_t row=0; row<sites*12; ++row) {
z[row] = w_acc[col*sites*12+row];
}
}
#endif
for (int i=0; i<Nu; ++i) {
assert(normalize(w[i],do_print)!=0);
}
Glog << "cuBLAS Zgemm done"<< std::endl;
cublasDestroy(handle);
Glog << "cuBLAS orthogonalize done" << std::endl;
#else
Glog << "BLAS wrapper is not implemented" << std::endl;
exit(1);
#endif
}
#if 0
void innerProductD (std::vector<ComplexD> &inner, std::vector<Field>& lhs, int llhs, std::vector<Field>& rhs, int lrhs)
{
typedef typename Field:vector_object vobj;
typedef typename vobj::scalar_type scalar_type;
typedef typename vobj::vector_typeD vector_type;
GridBase *grid = lhs[0]._grid;
assert(grid == rhs[0]._grid;
const int pad = 8;
int total = llhs*lrhs;
assert(inner.size()==total);
int sum_size=grid->SumArraySize();
// std::vector<ComplexD> inner(total);
Vector<ComplexD> sumarray(sum_size*pad*total);
parallel_for(int thr=0;thr<sum_size;thr++){
int nwork, mywork, myoff;
GridThread::GetWork(grid->oSites(),thr,mywork,myoff);
std::vector< decltype(innerProductD(lhs[0]._odata[0],rhs[0]._odata[0])) > vinner(total,zero); // private to thread; sub summation
for(int ss=myoff;ss<mywork+myoff; ss++){
for(int i=0; i<llhs; ++i){
for(int j=0; j<lrhs; ++j){
vinner[i*k+j] += innerProductD(lhs[i]._odata[ss],rhs[j]._odata[ss]);
}}
}
// All threads sum across SIMD; reduce serial work at end
// one write per cacheline with streaming store
for(int i=0; i<total; ++i){
ComplexD tmp = Reduce(TensorRemove(vinner[i])) ;
vstream(sumarray[(i*sum_size+thr)*pad],tmp);
}
}
for( int i =0;i<total;i++){
inner[i]=0.0;
for(int j=0;j<sum_size;j++){
inner[i] += sumarray[(i*sum_size+j)*pad];
}
}
for( int i =0;i<total;i++){
ComplexD tmp=inner[i];
evec[0]._grid->GlobalSum(tmp);
inner[i]=tmp;
}
// return inner;
}
#endif
void orthogonalize_blockhead(Field& w, std::vector<Field>& evec, int k, int Nu)
{
typedef typename Field::scalar_type MyComplex;
MyComplex ip;
for(int j=0; j<k; ++j){
ip = innerProduct(evec[j*Nu],w);
w = w - ip * evec[j*Nu];
}
normalize(w);
}
void calc(std::vector<RealD>& eval,
std::vector<Field>& evec,
const std::vector<Field>& src, int& Nconv, LanczosType Impl)
{
#ifdef GRID_NVCC
GridBase *grid = src[0].Grid();
grid->show_decomposition();
// set eigenvector buffers for the cuBLAS calls
//const uint64_t nsimd = grid->Nsimd();
const uint64_t sites = grid->lSites();
cudaStat = cudaMallocManaged((void **)&w_acc, Nu*sites*12*sizeof(cuDoubleComplex));
//Glog << cudaStat << std::endl;
cudaStat = cudaMallocManaged((void **)&evec_acc, Nevec_acc*sites*12*sizeof(cuDoubleComplex));
//Glog << cudaStat << std::endl;
cudaStat = cudaMallocManaged((void **)&c_acc, Nu*Nevec_acc*sizeof(cuDoubleComplex));
//Glog << cudaStat << std::endl;
#endif
switch (Impl) {
case LanczosType::irbl:
calc_irbl(eval,evec,src,Nconv);
break;
case LanczosType::rbl:
calc_rbl(eval,evec,src,Nconv);
break;
}
#ifdef GRID_NVCC
// free eigenvector buffers for the cuBLAS calls
cudaFree(w_acc);
cudaFree(evec_acc);
cudaFree(c_acc);
#endif
}
void calc_irbl(std::vector<RealD>& eval,
std::vector<Field>& evec,
const std::vector<Field>& src, int& Nconv)
{
std::string fname = std::string(cname+"::calc_irbl()");
GridBase *grid = evec[0].Grid();
assert(grid == src[0].Grid());
assert( Nu = src.size() );
Glog << std::string(74,'*') << std::endl;
Glog << fname + " starting iteration 0 / "<< MaxIter<< std::endl;
Glog << std::string(74,'*') << std::endl;
Glog <<" -- seek Nk = "<< Nk <<" vectors"<< std::endl;
Glog <<" -- accept Nstop = "<< Nstop <<" vectors"<< std::endl;
Glog <<" -- total Nm = "<< Nm <<" vectors"<< std::endl;
Glog <<" -- size of eval = "<< eval.size() << std::endl;
Glog <<" -- size of evec = "<< evec.size() << std::endl;
if ( diagonalisation == IRBLdiagonaliseWithEigen ) {
Glog << "Diagonalisation is Eigen "<< std::endl;
#ifdef USE_LAPACK
} else if ( diagonalisation == IRBLdiagonaliseWithLAPACK ) {
Glog << "Diagonalisation is LAPACK "<< std::endl;
#endif
} else {
abort();
}
Glog << std::string(74,'*') << std::endl;
assert(Nm == evec.size() && Nm == eval.size());
std::vector<std::vector<ComplexD>> lmd(Nu,std::vector<ComplexD>(Nm,0.0));
std::vector<std::vector<ComplexD>> lme(Nu,std::vector<ComplexD>(Nm,0.0));
std::vector<std::vector<ComplexD>> lmd2(Nu,std::vector<ComplexD>(Nm,0.0));
std::vector<std::vector<ComplexD>> lme2(Nu,std::vector<ComplexD>(Nm,0.0));
std::vector<RealD> eval2(Nm);
std::vector<RealD> resid(Nk);
Eigen::MatrixXcd Qt = Eigen::MatrixXcd::Zero(Nm,Nm);
Eigen::MatrixXcd Q = Eigen::MatrixXcd::Zero(Nm,Nm);
std::vector<int> Iconv(Nm);
std::vector<Field> B(Nm,grid); // waste of space replicating
std::vector<Field> f(Nu,grid);
std::vector<Field> f_copy(Nu,grid);
Field v(grid);
Nconv = 0;
RealD beta_k;
// set initial vector
for (int i=0; i<Nu; ++i) {
Glog << "norm2(src[" << i << "])= "<< norm2(src[i]) << std::endl;
evec[i] = src[i];
orthogonalize(evec[i],evec,i);
Glog << "norm2(evec[" << i << "])= "<< norm2(evec[i]) << std::endl;
}
// initial Nblock_k steps
for(int b=0; b<Nblock_k; ++b) blockwiseStep(lmd,lme,evec,f,f_copy,b);
// restarting loop begins
int iter;
for(iter = 0; iter<MaxIter; ++iter){
Glog <<"#Restart iteration = "<< iter << std::endl;
// additional (Nblock_m - Nblock_k) steps
for(int b=Nblock_k; b<Nblock_m; ++b) blockwiseStep(lmd,lme,evec,f,f_copy,b);
// getting eigenvalues
for(int u=0; u<Nu; ++u){
for(int k=0; k<Nm; ++k){
lmd2[u][k] = lmd[u][k];
lme2[u][k] = lme[u][k];
}
}
Qt = Eigen::MatrixXcd::Identity(Nm,Nm);
diagonalize(eval2,lmd2,lme2,Nu,Nm,Nm,Qt,grid);
_sort.push(eval2,Nm);
Glog << "#Ritz value before shift: "<< std::endl;
for(int i=0; i<Nm; ++i){
std::cout.precision(13);
std::cout << "[" << std::setw(4)<< std::setiosflags(std::ios_base::right) <<i<<"] ";
std::cout << "Rval = "<<std::setw(20)<< std::setiosflags(std::ios_base::left)<< eval2[i] << std::endl;
}
//----------------------------------------------------------------------
if ( Nm>Nk ) {
Glog <<" #Apply shifted QR transformations "<<std::endl;
//int k2 = Nk+Nu;
int k2 = Nk;
Eigen::MatrixXcd BTDM = Eigen::MatrixXcd::Identity(Nm,Nm);
Q = Eigen::MatrixXcd::Identity(Nm,Nm);
unpackHermitBlockTriDiagMatToEigen(lmd,lme,Nu,Nblock_m,Nm,Nm,BTDM);
for(int ip=Nk; ip<Nm; ++ip){
shiftedQRDecompEigen(BTDM,Nu,Nm,eval2[ip],Q);
}
packHermitBlockTriDiagMatfromEigen(lmd,lme,Nu,Nblock_m,Nm,Nm,BTDM);
for(int i=0; i<k2; ++i) B[i] = 0.0;
for(int j=0; j<k2; ++j){
for(int k=0; k<Nm; ++k){
B[j].Checkerboard() = evec[k].Checkerboard();
B[j] += evec[k]*Q(k,j);
}
}
for(int i=0; i<k2; ++i) evec[i] = B[i];
// reconstruct initial vector for additional pole space
blockwiseStep(lmd,lme,evec,f,f_copy,Nblock_k-1);
// getting eigenvalues
for(int u=0; u<Nu; ++u){
for(int k=0; k<Nm; ++k){
lmd2[u][k] = lmd[u][k];
lme2[u][k] = lme[u][k];
}
}
Qt = Eigen::MatrixXcd::Identity(Nm,Nm);
diagonalize(eval2,lmd2,lme2,Nu,Nk,Nm,Qt,grid);
_sort.push(eval2,Nk);
Glog << "#Ritz value after shift: "<< std::endl;
for(int i=0; i<Nk; ++i){
std::cout.precision(13);
std::cout << "[" << std::setw(4)<< std::setiosflags(std::ios_base::right) <<i<<"] ";
std::cout << "Rval = "<<std::setw(20)<< std::setiosflags(std::ios_base::left)<< eval2[i] << std::endl;
}
}
//----------------------------------------------------------------------
// Convergence test
Glog <<" #Convergence test: "<<std::endl;
for(int k = 0; k<Nk; ++k) B[k]=0.0;
for(int j = 0; j<Nk; ++j){
for(int k = 0; k<Nk; ++k){
B[j].Checkerboard() = evec[k].Checkerboard();
B[j] += evec[k]*Qt(k,j);
}
}
Nconv = 0;
for(int i=0; i<Nk; ++i){
_Linop.HermOp(B[i],v);
RealD vnum = real(innerProduct(B[i],v)); // HermOp.
RealD vden = norm2(B[i]);
eval2[i] = vnum/vden;
v -= eval2[i]*B[i];
RealD vv = norm2(v);
resid[i] = vv;
std::cout.precision(13);
std::cout << "[" << std::setw(4)<< std::setiosflags(std::ios_base::right) <<i<<"] ";
std::cout << "eval = "<<std::setw(20)<< std::setiosflags(std::ios_base::left)<< eval2[i];
std::cout << " resid^2 = "<< std::setw(20)<< std::setiosflags(std::ios_base::right)<< vv<< std::endl;
// change the criteria as evals are supposed to be sorted, all evals smaller(larger) than Nstop should have converged
//if( (vv<eresid*eresid) && (i == Nconv) ){
if (vv<eresid*eresid) {
Iconv[Nconv] = i;
++Nconv;
}
} // i-loop end
Glog <<" #modes converged: "<<Nconv<<std::endl;
for(int i=0; i<Nconv; ++i){
std::cout.precision(13);
std::cout << "[" << std::setw(4)<< std::setiosflags(std::ios_base::right) <<Iconv[i]<<"] ";
std::cout << "eval_conv = "<<std::setw(20)<< std::setiosflags(std::ios_base::left)<< eval2[Iconv[i]];
std::cout << " resid^2 = "<< std::setw(20)<< std::setiosflags(std::ios_base::right)<< resid[Iconv[i]]<< std::endl;
}
if ( Nconv>=Nstop ) break;
} // end of iter loop
Glog << std::string(74,'*') << std::endl;
if ( Nconv<Nstop ) {
Glog << fname + " NOT converged ; Summary :\n";
} else {
Glog << fname + " CONVERGED ; Summary :\n";
// Sort convered eigenpairs.
eval.resize(Nconv);
evec.resize(Nconv,grid);
for(int i=0; i<Nconv; ++i){
eval[i] = eval2[Iconv[i]];
evec[i] = B[Iconv[i]];
}
_sort.push(eval,evec,Nconv);
}
Glog << std::string(74,'*') << std::endl;
Glog << " -- Iterations = "<< iter << "\n";
//Glog << " -- beta(k) = "<< beta_k << "\n";
Glog << " -- Nconv = "<< Nconv << "\n";
Glog << std::string(74,'*') << std::endl;
}
void calc_rbl(std::vector<RealD>& eval,
std::vector<Field>& evec,
const std::vector<Field>& src, int& Nconv)
{
std::string fname = std::string(cname+"::calc_rbl()");
GridBase *grid = evec[0].Grid();
assert(grid == src[0].Grid());
assert( Nu = src.size() );
int Np = (Nm-Nk);
if (Np > 0 && MaxIter > 1) Np /= MaxIter;
int Nblock_p = Np/Nu;
Glog << std::string(74,'*') << std::endl;
Glog << fname + " starting iteration 0 / "<< MaxIter<< std::endl;
Glog << std::string(74,'*') << std::endl;
Glog <<" -- seek (min) Nk = "<< Nk <<" vectors"<< std::endl;
Glog <<" -- seek (inc) Np = "<< Np <<" vectors"<< std::endl;
Glog <<" -- seek (max) Nm = "<< Nm <<" vectors"<< std::endl;
Glog <<" -- accept Nstop = "<< Nstop <<" vectors"<< std::endl;
Glog <<" -- size of eval = "<< eval.size() << std::endl;
Glog <<" -- size of evec = "<< evec.size() << std::endl;
if ( diagonalisation == IRBLdiagonaliseWithEigen ) {
Glog << "Diagonalisation is Eigen "<< std::endl;
#ifdef USE_LAPACK
} else if ( diagonalisation == IRBLdiagonaliseWithLAPACK ) {
Glog << "Diagonalisation is LAPACK "<< std::endl;
#endif
} else {
abort();
}
Glog << std::string(74,'*') << std::endl;
assert(Nm == evec.size() && Nm == eval.size());
std::vector<std::vector<ComplexD>> lmd(Nu,std::vector<ComplexD>(Nm,0.0));
std::vector<std::vector<ComplexD>> lme(Nu,std::vector<ComplexD>(Nm,0.0));
std::vector<std::vector<ComplexD>> lmd2(Nu,std::vector<ComplexD>(Nm,0.0));
std::vector<std::vector<ComplexD>> lme2(Nu,std::vector<ComplexD>(Nm,0.0));
std::vector<RealD> eval2(Nk);
std::vector<RealD> resid(Nm);
Eigen::MatrixXcd Qt = Eigen::MatrixXcd::Zero(Nm,Nm);
Eigen::MatrixXcd Q = Eigen::MatrixXcd::Zero(Nm,Nm);
std::vector<int> Iconv(Nm);
std::vector<Field> B(Nm,grid); // waste of space replicating
std::vector<Field> f(Nu,grid);
std::vector<Field> f_copy(Nu,grid);
Field v(grid);
Nconv = 0;
RealD beta_k;
// set initial vector
for (int i=0; i<Nu; ++i) {
Glog << "norm2(src[" << i << "])= "<< norm2(src[i]) << std::endl;
evec[i] = src[i];
orthogonalize(evec[i],evec,i);
Glog << "norm2(evec[" << i << "])= "<< norm2(evec[i]) << std::endl;
}
// exit(-43);
// initial Nblock_k steps
for(int b=0; b<Nblock_k; ++b) blockwiseStep(lmd,lme,evec,f,f_copy,b);
// restarting loop begins
int iter;
int Nblock_l, Nblock_r;
int Nl, Nr;
int Nconv_guess = 0;
for(iter = 0; iter<MaxIter; ++iter){
Glog <<"#Restart iteration = "<< iter << std::endl;
Nblock_l = Nblock_k + iter*Nblock_p;
Nblock_r = Nblock_l + Nblock_p;
Nl = Nblock_l*Nu;
Nr = Nblock_r*Nu;
eval2.resize(Nr);
// additional Nblock_p steps
for(int b=Nblock_l; b<Nblock_r; ++b) blockwiseStep(lmd,lme,evec,f,f_copy,b);
// getting eigenvalues
for(int u=0; u<Nu; ++u){
for(int k=0; k<Nr; ++k){
lmd2[u][k] = lmd[u][k];
lme2[u][k] = lme[u][k];
}
}
Qt = Eigen::MatrixXcd::Identity(Nr,Nr);
diagonalize(eval2,lmd2,lme2,Nu,Nr,Nr,Qt,grid);
_sort.push(eval2,Nr);
Glog << "#Ritz value: "<< std::endl;
for(int i=0; i<Nr; ++i){
std::cout.precision(13);
std::cout << "[" << std::setw(4)<< std::setiosflags(std::ios_base::right) <<i<<"] ";
std::cout << "Rval = "<<std::setw(20)<< std::setiosflags(std::ios_base::left)<< eval2[i] << std::endl;
}
// Convergence test
Glog <<" #Convergence test: "<<std::endl;
Nconv = 0;
for(int k = 0; k<Nr; ++k) B[k]=0.0;
for(int j = 0; j<Nr; j+=Nconv_test_interval){
if ( j/Nconv_test_interval == Nconv ) {
Glog <<" #rotation for next check point evec"
<< std::setw(4)<< std::setiosflags(std::ios_base::right)
<< "["<< j <<"]" <<std::endl;
for(int k = 0; k<Nr; ++k){
B[j].Checkerboard() = evec[k].Checkerboard();
B[j] += evec[k]*Qt(k,j);
}
_Linop.HermOp(B[j],v);
RealD vnum = real(innerProduct(B[j],v)); // HermOp.
RealD vden = norm2(B[j]);
eval2[j] = vnum/vden;
v -= eval2[j]*B[j];
RealD vv = norm2(v);
resid[j] = vv;
std::cout.precision(13);
std::cout << "[" << std::setw(4)<< std::setiosflags(std::ios_base::right) <<j<<"] ";
std::cout << "eval = "<<std::setw(20)<< std::setiosflags(std::ios_base::left)<< eval2[j];
std::cout << " resid^2 = "<< std::setw(20)<< std::setiosflags(std::ios_base::right)<< vv<< std::endl;
// change the criteria as evals are supposed to be sorted, all evals smaller(larger) than Nstop should have converged
//if( (vv<eresid*eresid) && (i == Nconv) ){
if (vv<eresid*eresid) {
Iconv[Nconv] = j;
++Nconv;
}
} else {
break;
}
} // j-loop end
Glog <<" #modes converged: "<<Nconv<<std::endl;
for(int i=0; i<Nconv; ++i){
std::cout.precision(13);
std::cout << "[" << std::setw(4)<< std::setiosflags(std::ios_base::right) <<Iconv[i]<<"] ";
std::cout << "eval_conv = "<<std::setw(20)<< std::setiosflags(std::ios_base::left)<< eval2[Iconv[i]];
std::cout << " resid^2 = "<< std::setw(20)<< std::setiosflags(std::ios_base::right)<< resid[Iconv[i]]<< std::endl;
}
(Nconv > 0 ) ? Nconv_guess = 1 + (Nconv-1)*Nconv_test_interval : Nconv_guess = 0;
if ( Nconv_guess >= Nstop ) break;
} // end of iter loop
Glog << std::string(74,'*') << std::endl;
if ( Nconv_guess < Nstop ) {
Glog << fname + " NOT converged ; Summary :\n";
} else {
Glog << fname + " CONVERGED ; Summary :\n";
// Sort convered eigenpairs.
eval.resize(Nconv);
evec.resize(Nconv,grid);
for(int i=0; i<Nconv; ++i){
eval[i] = eval2[Iconv[i]];
evec[i] = B[Iconv[i]];
}
_sort.push(eval,evec,Nconv);
}
Glog << std::string(74,'*') << std::endl;
Glog << " -- Iterations = "<< iter << "\n";
//Glog << " -- beta(k) = "<< beta_k << "\n";
Glog << " -- Nconv = "<< Nconv << "\n";
Glog << " -- Nconv (guess) = "<< Nconv_guess << "\n";
Glog << std::string(74,'*') << std::endl;
}
private:
void blockwiseStep(std::vector<std::vector<ComplexD>>& lmd,
std::vector<std::vector<ComplexD>>& lme,
std::vector<Field>& evec,
std::vector<Field>& w,
std::vector<Field>& w_copy,
int b)
{
const RealD tiny = 1.0e-20;
int Nu = w.size();
int Nm = evec.size();
assert( b < Nm/Nu );
// GridCartesian *grid = evec[0]._grid;
// converts block index to full indicies for an interval [L,R)
int L = Nu*b;
int R = Nu*(b+1);
Real beta;
Glog << "Using split grid"<< std::endl;
// LatticeGaugeField s_Umu(SGrid);
assert((Nu%mrhs)==0);
std::vector<Field> in(mrhs,f_grid);
Field s_in(sf_grid);
Field s_out(sf_grid);
// unnecessary copy. Can or should it be avoided?
int k_start = 0;
while ( k_start < Nu) {
Glog << "k_start= "<<k_start<< std::endl;
for (int u=0; u<mrhs; ++u) in[u] = evec[L+k_start+u];
Glog << "Split "<< std::endl;
Grid_split(in, s_in);
Glog << "Split done "<< std::endl;
_poly(_SLinop,s_in,s_out);
Glog << "Unsplit "<< std::endl;
Grid_unsplit(in,s_out);
Glog << "Unsplit done "<< std::endl;
for (int u=0; u<mrhs; ++u) w[k_start+u] = in[u];
k_start +=mrhs;
}
Glog << "Using split grid done "<< std::endl;
// test split in the first iteration
if(!split_test){
Glog << "Not using split grid"<< std::endl;
// 3. wk:=Avkβkv_{k1}
for (int k=L, u=0; k<R; ++k, ++u) {
_poly(_Linop,evec[k],w_copy[u]);
}
Glog << "Not using split grid done"<< std::endl;
for (int u=0; u<Nu; ++u) {
w_copy[u] -= w[u];
Glog << "diff(split - non_split) "<<u<<" " << norm2(w_copy[u]) << std::endl;
}
split_test=1;
}
Glog << "Poly done"<< std::endl;
Glog << "LinAlg "<< std::endl;
// exit(-42);
if (b>0) {
for (int u=0; u<Nu; ++u) {
//for (int k=L-Nu; k<L; ++k) {
for (int k=L-Nu+u; k<L; ++k) {
w[u] = w[u] - evec[k] * conjugate(lme[u][k]);
}
}
}
// 4. αk:=(vk,wk)
//for (int u=0; u<Nu; ++u) {
// for (int k=L; k<R; ++k) {
// lmd[u][k] = innerProduct(evec[k],w[u]); // lmd = transpose of alpha
// }
// lmd[u][L+u] = real(lmd[u][L+u]); // force diagonal to be real
//}
for (int u=0; u<Nu; ++u) {
for (int k=L+u; k<R; ++k) {
lmd[u][k] = innerProduct(evec[k],w[u]); // lmd = transpose of alpha
// Glog <<"lmd "<<u<<" "<<k<<lmd[u][k] -conjugate(innerProduct(evec[u+L],w[k-L]))<<std::endl;
lmd[k-L][u+L] = conjugate(lmd[u][k]); // force hermicity
}
lmd[u][L+u] = real(lmd[u][L+u]); // force diagonal to be real
}
// 5. wk:=wk−αkvk
for (int u=0; u<Nu; ++u) {
for (int k=L; k<R; ++k) {
w[u] = w[u] - evec[k]*lmd[u][k];
}
w_copy[u] = w[u];
}
Glog << "LinAlg done"<< std::endl;
// In block version, the steps 6 and 7 in Lanczos construction is
// replaced by the QR decomposition of new basis block.
// It results block version beta and orthonormal block basis.
// Here, QR decomposition is done by using Gram-Schmidt.
for (int u=0; u<Nu; ++u) {
for (int k=L; k<R; ++k) {
lme[u][k] = 0.0;
}
}
Glog << "Gram Schmidt"<< std::endl;
// re-orthogonalization for numerical stability
#if 0
for (int u=0; u<Nu; ++u) {
orthogonalize(w[u],evec,R);
}
#else
//orthogonalize(w,Nu,evec,R);
orthogonalize_blas(w,evec,R);
#endif
// QR part
for (int u=1; u<Nu; ++u) {
orthogonalize(w[u],w,u);
}
Glog << "Gram Schmidt done "<< std::endl;
Glog << "LinAlg "<< std::endl;
for (int u=0; u<Nu; ++u) {
//for (int v=0; v<Nu; ++v) {
for (int v=u; v<Nu; ++v) {
lme[u][L+v] = innerProduct(w[u],w_copy[v]);
}
lme[u][L+u] = real(lme[u][L+u]); // force diagonal to be real
}
//lme[0][L] = beta;
for (int u=0; u<Nu; ++u) {
Glog << "norm2(w[" << u << "])= "<< norm2(w[u]) << std::endl;
assert (!isnan(norm2(w[u])));
for (int k=L+u; k<R; ++k) {
Glog <<" In block "<< b << ",";
std::cout <<" beta[" << u << "," << k-L << "] = ";
std::cout << lme[u][k] << std::endl;
}
}
Glog << "LinAlg done "<< std::endl;
#if 0
Glog << "Gram Schmidt "<< std::endl;
// re-orthogonalization for numerical stability
if (b>0) {
for (int u=0; u<Nu; ++u) {
orthogonalize(w[u],evec,R);
}
for (int u=1; u<Nu; ++u) {
orthogonalize(w[u],w,u);
}
}
//if (b>0) {
// orthogonalize_blockhead(w[0],evec,b,Nu);
// for (int u=1; u<Nu; ++u) {
// orthogonalize(w[u],w,u);
// }
//}
Glog << "Gram Schmidt done "<< std::endl;
#endif
if (b < Nm/Nu-1) {
for (int u=0; u<Nu; ++u) {
evec[R+u] = w[u];
}
}
}
void diagonalize_Eigen(std::vector<RealD>& eval,
std::vector<std::vector<ComplexD>>& lmd,
std::vector<std::vector<ComplexD>>& lme,
int Nu, int Nk, int Nm,
Eigen::MatrixXcd & Qt, // Nm x Nm
GridBase *grid)
{
assert( Nk%Nu == 0 && Nm%Nu == 0 );
assert( Nk <= Nm );
Eigen::MatrixXcd BlockTriDiag = Eigen::MatrixXcd::Zero(Nk,Nk);
for ( int u=0; u<Nu; ++u ) {
for (int k=0; k<Nk; ++k ) {
BlockTriDiag(k,u+(k/Nu)*Nu) = lmd[u][k];
}
}
for ( int u=0; u<Nu; ++u ) {
for (int k=Nu; k<Nk; ++k ) {
BlockTriDiag(k-Nu,u+(k/Nu)*Nu) = conjugate(lme[u][k-Nu]);
BlockTriDiag(u+(k/Nu)*Nu,k-Nu) = lme[u][k-Nu];
}
}
//std::cout << BlockTriDiag << std::endl;
Eigen::SelfAdjointEigenSolver<Eigen::MatrixXcd> eigensolver(BlockTriDiag);
for (int i = 0; i < Nk; i++) {
eval[Nk-1-i] = eigensolver.eigenvalues()(i);
}
for (int i = 0; i < Nk; i++) {
for (int j = 0; j < Nk; j++) {
Qt(j,Nk-1-i) = eigensolver.eigenvectors()(j,i);
//Qt(Nk-1-i,j) = eigensolver.eigenvectors()(i,j);
//Qt(i,j) = eigensolver.eigenvectors()(i,j);
}
}
}
#ifdef USE_LAPACK
void diagonalize_lapack(std::vector<RealD>& eval,
std::vector<std::vector<ComplexD>>& lmd,
std::vector<std::vector<ComplexD>>& lme,
int Nu, int Nk, int Nm,
Eigen::MatrixXcd & Qt, // Nm x Nm
GridBase *grid)
{
Glog << "diagonalize_lapack: Nu= "<<Nu<<" Nk= "<<Nk<<" Nm= "<<std::endl;
assert( Nk%Nu == 0 && Nm%Nu == 0 );
assert( Nk <= Nm );
Eigen::MatrixXcd BlockTriDiag = Eigen::MatrixXcd::Zero(Nk,Nk);
for ( int u=0; u<Nu; ++u ) {
for (int k=0; k<Nk; ++k ) {
// Glog << "lmd "<<u<<" "<<k<<" "<<lmd[u][k] -conjugate(lmd[u][k])<<std::endl;
BlockTriDiag(k,u+(k/Nu)*Nu) = lmd[u][k];
}
}
for ( int u=0; u<Nu; ++u ) {
for (int k=Nu; k<Nk; ++k ) {
// Glog << "lme "<<u<<" "<<k<<" "<<lme[u][k] -conjugate(lme[u][k])<<std::endl;
BlockTriDiag(k-Nu,u+(k/Nu)*Nu) = conjugate(lme[u][k-Nu]);
BlockTriDiag(u+(k/Nu)*Nu,k-Nu) = lme[u][k-Nu];
}
}
//std::cout << BlockTriDiag << std::endl;
//#ifdef USE_LAPACK
#if 1
const int size = Nm;
MKL_INT NN = Nk;
// double evals_tmp[NN];
// double evec_tmp[NN][NN];
double *evals_tmp = (double *) malloc(NN*sizeof(double));
MKL_Complex16 *evec_tmp = (MKL_Complex16 *) malloc(NN*NN*sizeof(MKL_Complex16));
MKL_Complex16 *DD = (MKL_Complex16 *) malloc(NN*NN*sizeof(MKL_Complex16));
for (int i = 0; i< NN; i++) {
for (int j = 0; j <NN ; j++) {
evec_tmp[i*NN+j].real=0.;
evec_tmp[i*NN+j].imag=0.;
DD[i*NN+j].real=BlockTriDiag(i,j).real();
DD[i*NN+j].imag=BlockTriDiag(i,j).imag();
}
}
MKL_INT evals_found;
MKL_INT lwork = (3*NN);
MKL_INT lrwork = (24*NN);
MKL_INT liwork = NN*10 ;
MKL_INT iwork[liwork];
double rwork[lrwork];
// double work[lwork];
MKL_Complex16 *work = (MKL_Complex16 *) malloc(lwork*sizeof(MKL_Complex16));
MKL_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];
MKL_INT info;
int total = grid->_Nprocessors;
int node = grid->_processor;
int interval = (NN/total)+1;
double vl = 0.0, vu = 0.0;
MKL_INT il = interval*node+1 , iu = interval*(node+1);
if (iu > NN) iu=NN;
Glog << "node "<<node<<"il "<<il<<"iu "<<iu<<std::endl;
double tol = 0.0;
if (1) {
memset(evals_tmp,0,sizeof(double)*NN);
if ( il <= NN){
zheevr(&jobz, &range, &uplo, &NN,
DD, &NN,
&vl, &vu, &il, &iu, // these four are ignored if second parameteris 'A'
&tol, // tolerance
&evals_found, evals_tmp, (MKL_Complex16*)evec_tmp, &NN,
isuppz,
work, &lwork,
rwork, &lrwork,
iwork, &liwork,
&info);
// (double*)EE,
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*NN+j] = evec_tmp[(i - (il-1))*NN+j];
if (il>1) {
evec_tmp[(i-(il-1))*NN+j].imag=0.;
evec_tmp[(i-(il-1))*NN+j].real=0.;
}
}
}
}
{
grid->GlobalSumVector(evals_tmp,NN);
grid->GlobalSumVector((double*)evec_tmp,2*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];
// }
// }
// MKL_Complex16 *eval_tmp = malloc(NN*sizeof(MKL_Complex16));
// MKL_Complex16 *evec_tmp = malloc(NN*NN*sizeof(MKL_Complex16));
// MKL_Complex16 *DD = malloc(NN*NN*sizeof(MKL_Complex16));
#endif
for (int i = 0; i < Nk; i++)
eval[Nk-1-i] = evals_tmp[i];
for (int i = 0; i < Nk; i++) {
for (int j = 0; j < Nk; j++) {
// Qt(j,Nk-1-i) = eigensolver.eigenvectors()(j,i);
Qt(j,Nk-1-i)=std::complex<double>
( evec_tmp[i*Nk+j].real,
evec_tmp[i*Nk+j].imag);
// ( evec_tmp[(Nk-1-j)*Nk+Nk-1-i].real,
// evec_tmp[(Nk-1-j)*Nk+Nk-1-i].imag);
}
}
if (1){
Eigen::SelfAdjointEigenSolver<Eigen::MatrixXcd> eigensolver(BlockTriDiag);
for (int i = 0; i < Nk; i++) {
Glog << "eval = "<<i<<" " <<eval[Nk-1-i] <<" "<< eigensolver.eigenvalues()(i) <<std::endl;
// eval[Nk-1-i] = eigensolver.eigenvalues()(i);
}
for (int i = 0; i < Nk; i++) {
for (int j = 0; j < Nk; j++) {
// Qt(j,Nk-1-i) = eigensolver.eigenvectors()(j,i);
// Glog<<"Qt "<<j<<" "<<Nk-1-i<<" = " <<Qt(j,Nk-1-i) <<" "<<eigensolver.eigenvectors()(j,i) <<std::endl;
MKL_Complex16 tmp = evec_tmp[i*Nk+j];
// Glog<<"Qt "<<j<<" "<<Nk-1-i<<" = " <<evec_tmp[(Nk-1-j)*Nk+Nk-1-i].real<<" "<<
//evec_tmp[(Nk-1-j)*Nk+Nk-1-i].imag <<" "<<eigensolver.eigenvectors()(j,i) <<std::endl;
if ( (i<5)&& (j<5))
Glog<<"Qt "<<j<<" "<<Nk-1-i<<" = " << norm(Qt(j,Nk-1-i))<<" "<<
norm(eigensolver.eigenvectors()(j,i)) <<std::endl;
}
}
}
// exit(-43);
free (evals_tmp);
free (evec_tmp);
free (DD);
free (work);
}
#endif
void diagonalize(std::vector<RealD>& eval,
std::vector<std::vector<ComplexD>>& lmd,
std::vector<std::vector<ComplexD>>& lme,
int Nu, int Nk, int Nm,
Eigen::MatrixXcd & Qt,
GridBase *grid)
{
Qt = Eigen::MatrixXcd::Identity(Nm,Nm);
if ( diagonalisation == IRBLdiagonaliseWithEigen ) {
diagonalize_Eigen(eval,lmd,lme,Nu,Nk,Nm,Qt,grid);
#ifdef USE_LAPACK
} else if ( diagonalisation == IRBLdiagonaliseWithLAPACK ) {
diagonalize_lapack(eval,lmd,lme,Nu,Nk,Nm,Qt,grid);
#endif
} else {
assert(0);
}
}
void unpackHermitBlockTriDiagMatToEigen(
std::vector<std::vector<ComplexD>>& lmd,
std::vector<std::vector<ComplexD>>& lme,
int Nu, int Nb, int Nk, int Nm,
Eigen::MatrixXcd& M)
{
//Glog << "unpackHermitBlockTriDiagMatToEigen() begin" << '\n';
assert( Nk%Nu == 0 && Nm%Nu == 0 );
assert( Nk <= Nm );
M = Eigen::MatrixXcd::Zero(Nk,Nk);
// rearrange
for ( int u=0; u<Nu; ++u ) {
for (int k=0; k<Nk; ++k ) {
M(k,u+(k/Nu)*Nu) = lmd[u][k];
}
}
for ( int u=0; u<Nu; ++u ) {
for (int k=Nu; k<Nk; ++k ) {
M(k-Nu,u+(k/Nu)*Nu) = conjugate(lme[u][k-Nu]);
M(u+(k/Nu)*Nu,k-Nu) = lme[u][k-Nu];
}
}
//Glog << "unpackHermitBlockTriDiagMatToEigen() end" << endl;
}
void packHermitBlockTriDiagMatfromEigen(
std::vector<std::vector<ComplexD>>& lmd,
std::vector<std::vector<ComplexD>>& lme,
int Nu, int Nb, int Nk, int Nm,
Eigen::MatrixXcd& M)
{
//Glog << "packHermitBlockTriDiagMatfromEigen() begin" << '\n';
assert( Nk%Nu == 0 && Nm%Nu == 0 );
assert( Nk <= Nm );
// rearrange
for ( int u=0; u<Nu; ++u ) {
for (int k=0; k<Nk; ++k ) {
lmd[u][k] = M(k,u+(k/Nu)*Nu);
}
}
for ( int u=0; u<Nu; ++u ) {
for (int k=Nu; k<Nk; ++k ) {
lme[u][k-Nu] = M(u+(k/Nu)*Nu,k-Nu);
}
}
//Glog << "packHermitBlockTriDiagMatfromEigen() end" << endl;
}
// assume the input matrix M is a band matrix
void shiftedQRDecompEigen(Eigen::MatrixXcd& M, int Nu, int Nm,
RealD Dsh,
Eigen::MatrixXcd& Qprod)
{
//Glog << "shiftedQRDecompEigen() begin" << '\n';
Eigen::MatrixXcd Q = Eigen::MatrixXcd::Zero(Nm,Nm);
Eigen::MatrixXcd R = Eigen::MatrixXcd::Zero(Nm,Nm);
Eigen::MatrixXcd Mtmp = Eigen::MatrixXcd::Zero(Nm,Nm);
Mtmp = M;
for (int i=0; i<Nm; ++i ) {
Mtmp(i,i) = M(i,i) - Dsh;
}
Eigen::HouseholderQR<Eigen::MatrixXcd> QRD(Mtmp);
Q = QRD.householderQ();
R = QRD.matrixQR(); // upper triangular part is the R matrix.
// lower triangular part used to represent series
// of Q sequence.
// equivalent operation of Qprod *= Q
//M = Eigen::MatrixXcd::Zero(Nm,Nm);
//for (int i=0; i<Nm; ++i) {
// for (int j=0; j<Nm-2*(Nu+1); ++j) {
// for (int k=0; k<2*(Nu+1)+j; ++k) {
// M(i,j) += Qprod(i,k)*Q(k,j);
// }
// }
//}
//for (int i=0; i<Nm; ++i) {
// for (int j=Nm-2*(Nu+1); j<Nm; ++j) {
// for (int k=0; k<Nm; ++k) {
// M(i,j) += Qprod(i,k)*Q(k,j);
// }
// }
//}
Mtmp = Eigen::MatrixXcd::Zero(Nm,Nm);
for (int i=0; i<Nm; ++i) {
for (int j=0; j<Nm-(Nu+1); ++j) {
for (int k=0; k<Nu+1+j; ++k) {
Mtmp(i,j) += Qprod(i,k)*Q(k,j);
}
}
}
for (int i=0; i<Nm; ++i) {
for (int j=Nm-(Nu+1); j<Nm; ++j) {
for (int k=0; k<Nm; ++k) {
Mtmp(i,j) += Qprod(i,k)*Q(k,j);
}
}
}
//static int ntimes = 2;
//for (int j=0; j<Nm-(ntimes*Nu); ++j) {
// for (int i=ntimes*Nu+j; i<Nm; ++i) {
// Mtmp(i,j) = 0.0;
// }
//}
//ntimes++;
Qprod = Mtmp;
// equivalent operation of M = Q.adjoint()*(M*Q)
Mtmp = Eigen::MatrixXcd::Zero(Nm,Nm);
for (int a=0, i=0, kmax=0; a<Nu+1; ++a) {
for (int j=0; j<Nm-a; ++j) {
i = j+a;
kmax = (Nu+1)+j;
if (kmax > Nm) kmax = Nm;
for (int k=i; k<kmax; ++k) {
Mtmp(i,j) += R(i,k)*Q(k,j);
}
Mtmp(j,i) = conj(Mtmp(i,j));
}
}
for (int i=0; i<Nm; ++i) {
Mtmp(i,i) = real(Mtmp(i,i)) + Dsh;
}
M = Mtmp;
//M = Q.adjoint()*(M*Q);
//for (int i=0; i<Nm; ++i) {
// for (int j=0; j<Nm; ++j) {
// if (i==j) M(i,i) = real(M(i,i));
// if (j>i) M(i,j) = conj(M(j,i));
// if (i-j > Nu || j-i > Nu) M(i,j) = 0.;
// }
//}
//Glog << "shiftedQRDecompEigen() end" << endl;
}
void exampleQRDecompEigen(void)
{
Eigen::MatrixXd A = Eigen::MatrixXd::Zero(3,3);
Eigen::MatrixXd Q = Eigen::MatrixXd::Zero(3,3);
Eigen::MatrixXd R = Eigen::MatrixXd::Zero(3,3);
Eigen::MatrixXd P = Eigen::MatrixXd::Zero(3,3);
A(0,0) = 12.0;
A(0,1) = -51.0;
A(0,2) = 4.0;
A(1,0) = 6.0;
A(1,1) = 167.0;
A(1,2) = -68.0;
A(2,0) = -4.0;
A(2,1) = 24.0;
A(2,2) = -41.0;
Glog << "matrix A before ColPivHouseholder" << std::endl;
for ( int i=0; i<3; i++ ) {
for ( int j=0; j<3; j++ ) {
Glog << "A[" << i << "," << j << "] = " << A(i,j) << '\n';
}
}
Glog << std::endl;
Eigen::ColPivHouseholderQR<Eigen::MatrixXd> QRD(A);
Glog << "matrix A after ColPivHouseholder" << std::endl;
for ( int i=0; i<3; i++ ) {
for ( int j=0; j<3; j++ ) {
Glog << "A[" << i << "," << j << "] = " << A(i,j) << '\n';
}
}
Glog << std::endl;
Glog << "HouseholderQ with sequence lenth = nonzeroPiviots" << std::endl;
Q = QRD.householderQ().setLength(QRD.nonzeroPivots());
for ( int i=0; i<3; i++ ) {
for ( int j=0; j<3; j++ ) {
Glog << "Q[" << i << "," << j << "] = " << Q(i,j) << '\n';
}
}
Glog << std::endl;
Glog << "HouseholderQ with sequence lenth = 1" << std::endl;
Q = QRD.householderQ().setLength(1);
for ( int i=0; i<3; i++ ) {
for ( int j=0; j<3; j++ ) {
Glog << "Q[" << i << "," << j << "] = " << Q(i,j) << '\n';
}
}
Glog << std::endl;
Glog << "HouseholderQ with sequence lenth = 2" << std::endl;
Q = QRD.householderQ().setLength(2);
for ( int i=0; i<3; i++ ) {
for ( int j=0; j<3; j++ ) {
Glog << "Q[" << i << "," << j << "] = " << Q(i,j) << '\n';
}
}
Glog << std::endl;
Glog << "matrixR" << std::endl;
R = QRD.matrixR();
for ( int i=0; i<3; i++ ) {
for ( int j=0; j<3; j++ ) {
Glog << "R[" << i << "," << j << "] = " << R(i,j) << '\n';
}
}
Glog << std::endl;
Glog << "rank = " << QRD.rank() << std::endl;
Glog << "threshold = " << QRD.threshold() << std::endl;
Glog << "matrixP" << std::endl;
P = QRD.colsPermutation();
for ( int i=0; i<3; i++ ) {
for ( int j=0; j<3; j++ ) {
Glog << "P[" << i << "," << j << "] = " << P(i,j) << '\n';
}
}
Glog << std::endl;
Glog << "QR decomposition without column pivoting" << std::endl;
A(0,0) = 12.0;
A(0,1) = -51.0;
A(0,2) = 4.0;
A(1,0) = 6.0;
A(1,1) = 167.0;
A(1,2) = -68.0;
A(2,0) = -4.0;
A(2,1) = 24.0;
A(2,2) = -41.0;
Glog << "matrix A before Householder" << std::endl;
for ( int i=0; i<3; i++ ) {
for ( int j=0; j<3; j++ ) {
Glog << "A[" << i << "," << j << "] = " << A(i,j) << '\n';
}
}
Glog << std::endl;
Eigen::HouseholderQR<Eigen::MatrixXd> QRDplain(A);
Glog << "HouseholderQ" << std::endl;
Q = QRDplain.householderQ();
for ( int i=0; i<3; i++ ) {
for ( int j=0; j<3; j++ ) {
Glog << "Q[" << i << "," << j << "] = " << Q(i,j) << '\n';
}
}
Glog << std::endl;
Glog << "matrix A after Householder" << std::endl;
for ( int i=0; i<3; i++ ) {
for ( int j=0; j<3; j++ ) {
Glog << "A[" << i << "," << j << "] = " << A(i,j) << '\n';
}
}
Glog << std::endl;
}
};
}
#undef Glog
#undef USE_LAPACK
#endif
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