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Merge branch 'develop' into feature/hmc_generalise
This commit is contained in:
Guido Cossu
@@ -46,7 +46,7 @@ Author: Peter Boyle <paboyle@ph.ed.ac.uk>
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#include <Grid/algorithms/iterative/ConjugateGradientMixedPrec.h>
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// Lanczos support
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#include <Grid/algorithms/iterative/MatrixUtils.h>
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//#include <Grid/algorithms/iterative/MatrixUtils.h>
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#include <Grid/algorithms/iterative/ImplicitlyRestartedLanczos.h>
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#include <Grid/algorithms/CoarsenedMatrix.h>
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#include <Grid/algorithms/FFT.h>
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366
lib/algorithms/iterative/BlockConjugateGradient.h
Normal file
366
lib/algorithms/iterative/BlockConjugateGradient.h
Normal file
@@ -0,0 +1,366 @@
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/*************************************************************************************
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Grid physics library, www.github.com/paboyle/Grid
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Source file: ./lib/algorithms/iterative/BlockConjugateGradient.h
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Copyright (C) 2017
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Author: Azusa Yamaguchi <ayamaguc@staffmail.ed.ac.uk>
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Author: Peter Boyle <paboyle@ph.ed.ac.uk>
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|
||||
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.
|
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|
||||
This program is distributed in the hope that it will be useful,
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||||
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.
|
||||
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See the full license in the file "LICENSE" in the top level distribution
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directory
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*************************************************************************************/
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/* END LEGAL */
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#ifndef GRID_BLOCK_CONJUGATE_GRADIENT_H
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#define GRID_BLOCK_CONJUGATE_GRADIENT_H
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namespace Grid {
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//////////////////////////////////////////////////////////////////////////
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// Block conjugate gradient. Dimension zero should be the block direction
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//////////////////////////////////////////////////////////////////////////
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template <class Field>
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class BlockConjugateGradient : public OperatorFunction<Field> {
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public:
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typedef typename Field::scalar_type scomplex;
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const int blockDim = 0;
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int Nblock;
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bool ErrorOnNoConverge; // throw an assert when the CG fails to converge.
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// Defaults true.
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RealD Tolerance;
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Integer MaxIterations;
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Integer IterationsToComplete; //Number of iterations the CG took to finish. Filled in upon completion
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BlockConjugateGradient(RealD tol, Integer maxit, bool err_on_no_conv = true)
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: Tolerance(tol),
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MaxIterations(maxit),
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ErrorOnNoConverge(err_on_no_conv){};
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void operator()(LinearOperatorBase<Field> &Linop, const Field &Src, Field &Psi)
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{
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int Orthog = 0; // First dimension is block dim
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Nblock = Src._grid->_fdimensions[Orthog];
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std::cout<<GridLogMessage<<" Block Conjugate Gradient : Orthog "<<Orthog<<" Nblock "<<Nblock<<std::endl;
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Psi.checkerboard = Src.checkerboard;
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conformable(Psi, Src);
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Field P(Src);
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Field AP(Src);
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Field R(Src);
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Eigen::MatrixXcd m_pAp = Eigen::MatrixXcd::Identity(Nblock,Nblock);
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Eigen::MatrixXcd m_pAp_inv= Eigen::MatrixXcd::Identity(Nblock,Nblock);
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Eigen::MatrixXcd m_rr = Eigen::MatrixXcd::Zero(Nblock,Nblock);
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Eigen::MatrixXcd m_rr_inv = Eigen::MatrixXcd::Zero(Nblock,Nblock);
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Eigen::MatrixXcd m_alpha = Eigen::MatrixXcd::Zero(Nblock,Nblock);
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Eigen::MatrixXcd m_beta = Eigen::MatrixXcd::Zero(Nblock,Nblock);
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// Initial residual computation & set up
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std::vector<RealD> residuals(Nblock);
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std::vector<RealD> ssq(Nblock);
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sliceNorm(ssq,Src,Orthog);
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RealD sssum=0;
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for(int b=0;b<Nblock;b++) sssum+=ssq[b];
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sliceNorm(residuals,Src,Orthog);
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for(int b=0;b<Nblock;b++){ assert(std::isnan(residuals[b])==0); }
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sliceNorm(residuals,Psi,Orthog);
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for(int b=0;b<Nblock;b++){ assert(std::isnan(residuals[b])==0); }
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// Initial search dir is guess
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Linop.HermOp(Psi, AP);
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/************************************************************************
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* Block conjugate gradient (Stephen Pickles, thesis 1995, pp 71, O Leary 1980)
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************************************************************************
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* O'Leary : R = B - A X
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* O'Leary : P = M R ; preconditioner M = 1
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* O'Leary : alpha = PAP^{-1} RMR
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* O'Leary : beta = RMR^{-1}_old RMR_new
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* O'Leary : X=X+Palpha
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* O'Leary : R_new=R_old-AP alpha
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* O'Leary : P=MR_new+P beta
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*/
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R = Src - AP;
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P = R;
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sliceInnerProductMatrix(m_rr,R,R,Orthog);
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GridStopWatch sliceInnerTimer;
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GridStopWatch sliceMaddTimer;
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GridStopWatch MatrixTimer;
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GridStopWatch SolverTimer;
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SolverTimer.Start();
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int k;
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for (k = 1; k <= MaxIterations; k++) {
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RealD rrsum=0;
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for(int b=0;b<Nblock;b++) rrsum+=real(m_rr(b,b));
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std::cout << GridLogIterative << "\titeration "<<k<<" rr_sum "<<rrsum<<" ssq_sum "<< sssum
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<<" / "<<std::sqrt(rrsum/sssum) <<std::endl;
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MatrixTimer.Start();
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Linop.HermOp(P, AP);
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MatrixTimer.Stop();
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// Alpha
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sliceInnerTimer.Start();
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sliceInnerProductMatrix(m_pAp,P,AP,Orthog);
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sliceInnerTimer.Stop();
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m_pAp_inv = m_pAp.inverse();
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m_alpha = m_pAp_inv * m_rr ;
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// Psi, R update
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sliceMaddTimer.Start();
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sliceMaddMatrix(Psi,m_alpha, P,Psi,Orthog); // add alpha * P to psi
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sliceMaddMatrix(R ,m_alpha,AP, R,Orthog,-1.0);// sub alpha * AP to resid
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sliceMaddTimer.Stop();
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// Beta
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m_rr_inv = m_rr.inverse();
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sliceInnerTimer.Start();
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sliceInnerProductMatrix(m_rr,R,R,Orthog);
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sliceInnerTimer.Stop();
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m_beta = m_rr_inv *m_rr;
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// Search update
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sliceMaddTimer.Start();
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sliceMaddMatrix(AP,m_beta,P,R,Orthog);
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sliceMaddTimer.Stop();
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P= AP;
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/*********************
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* convergence monitor
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*********************
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*/
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RealD max_resid=0;
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for(int b=0;b<Nblock;b++){
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RealD rr = real(m_rr(b,b))/ssq[b];
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if ( rr > max_resid ) max_resid = rr;
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}
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if ( max_resid < Tolerance*Tolerance ) {
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SolverTimer.Stop();
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std::cout << GridLogMessage<<"BlockCG converged in "<<k<<" iterations"<<std::endl;
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for(int b=0;b<Nblock;b++){
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std::cout << GridLogMessage<< "\t\tblock "<<b<<" resid "<< std::sqrt(real(m_rr(b,b))/ssq[b])<<std::endl;
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}
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std::cout << GridLogMessage<<"\tMax residual is "<<std::sqrt(max_resid)<<std::endl;
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Linop.HermOp(Psi, AP);
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AP = AP-Src;
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std::cout << GridLogMessage <<"\tTrue residual is " << std::sqrt(norm2(AP)/norm2(Src)) <<std::endl;
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std::cout << GridLogMessage << "Time Breakdown "<<std::endl;
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std::cout << GridLogMessage << "\tElapsed " << SolverTimer.Elapsed() <<std::endl;
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std::cout << GridLogMessage << "\tMatrix " << MatrixTimer.Elapsed() <<std::endl;
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std::cout << GridLogMessage << "\tInnerProd " << sliceInnerTimer.Elapsed() <<std::endl;
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std::cout << GridLogMessage << "\tMaddMatrix " << sliceMaddTimer.Elapsed() <<std::endl;
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IterationsToComplete = k;
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return;
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}
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}
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std::cout << GridLogMessage << "BlockConjugateGradient did NOT converge" << std::endl;
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if (ErrorOnNoConverge) assert(0);
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IterationsToComplete = k;
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}
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};
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//////////////////////////////////////////////////////////////////////////
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// multiRHS conjugate gradient. Dimension zero should be the block direction
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//////////////////////////////////////////////////////////////////////////
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template <class Field>
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class MultiRHSConjugateGradient : public OperatorFunction<Field> {
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public:
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typedef typename Field::scalar_type scomplex;
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|
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const int blockDim = 0;
|
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|
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int Nblock;
|
||||
bool ErrorOnNoConverge; // throw an assert when the CG fails to converge.
|
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// Defaults true.
|
||||
RealD Tolerance;
|
||||
Integer MaxIterations;
|
||||
Integer IterationsToComplete; //Number of iterations the CG took to finish. Filled in upon completion
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|
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MultiRHSConjugateGradient(RealD tol, Integer maxit, bool err_on_no_conv = true)
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: Tolerance(tol),
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MaxIterations(maxit),
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ErrorOnNoConverge(err_on_no_conv){};
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|
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void operator()(LinearOperatorBase<Field> &Linop, const Field &Src, Field &Psi)
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{
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int Orthog = 0; // First dimension is block dim
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Nblock = Src._grid->_fdimensions[Orthog];
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std::cout<<GridLogMessage<<"MultiRHS Conjugate Gradient : Orthog "<<Orthog<<" Nblock "<<Nblock<<std::endl;
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Psi.checkerboard = Src.checkerboard;
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conformable(Psi, Src);
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|
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Field P(Src);
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Field AP(Src);
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Field R(Src);
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std::vector<ComplexD> v_pAp(Nblock);
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std::vector<RealD> v_rr (Nblock);
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std::vector<RealD> v_rr_inv(Nblock);
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std::vector<RealD> v_alpha(Nblock);
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std::vector<RealD> v_beta(Nblock);
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|
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// Initial residual computation & set up
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std::vector<RealD> residuals(Nblock);
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std::vector<RealD> ssq(Nblock);
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|
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sliceNorm(ssq,Src,Orthog);
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RealD sssum=0;
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for(int b=0;b<Nblock;b++) sssum+=ssq[b];
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sliceNorm(residuals,Src,Orthog);
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for(int b=0;b<Nblock;b++){ assert(std::isnan(residuals[b])==0); }
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|
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sliceNorm(residuals,Psi,Orthog);
|
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for(int b=0;b<Nblock;b++){ assert(std::isnan(residuals[b])==0); }
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|
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// Initial search dir is guess
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Linop.HermOp(Psi, AP);
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R = Src - AP;
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P = R;
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sliceNorm(v_rr,R,Orthog);
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GridStopWatch sliceInnerTimer;
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GridStopWatch sliceMaddTimer;
|
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GridStopWatch sliceNormTimer;
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GridStopWatch MatrixTimer;
|
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GridStopWatch SolverTimer;
|
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|
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SolverTimer.Start();
|
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int k;
|
||||
for (k = 1; k <= MaxIterations; k++) {
|
||||
|
||||
RealD rrsum=0;
|
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for(int b=0;b<Nblock;b++) rrsum+=real(v_rr[b]);
|
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|
||||
std::cout << GridLogIterative << "\titeration "<<k<<" rr_sum "<<rrsum<<" ssq_sum "<< sssum
|
||||
<<" / "<<std::sqrt(rrsum/sssum) <<std::endl;
|
||||
|
||||
MatrixTimer.Start();
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||||
Linop.HermOp(P, AP);
|
||||
MatrixTimer.Stop();
|
||||
|
||||
// Alpha
|
||||
// sliceInnerProductVectorTest(v_pAp_test,P,AP,Orthog);
|
||||
sliceInnerTimer.Start();
|
||||
sliceInnerProductVector(v_pAp,P,AP,Orthog);
|
||||
sliceInnerTimer.Stop();
|
||||
for(int b=0;b<Nblock;b++){
|
||||
// std::cout << " "<< v_pAp[b]<<" "<< v_pAp_test[b]<<std::endl;
|
||||
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<<" 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;
|
||||
}
|
||||
};
|
||||
|
||||
|
||||
|
||||
}
|
||||
#endif
|
||||
@@ -78,18 +78,12 @@ class ConjugateGradient : public OperatorFunction<Field> {
|
||||
cp = a;
|
||||
ssq = norm2(src);
|
||||
|
||||
std::cout << GridLogIterative << std::setprecision(4)
|
||||
<< "ConjugateGradient: guess " << guess << std::endl;
|
||||
std::cout << GridLogIterative << std::setprecision(4)
|
||||
<< "ConjugateGradient: src " << ssq << std::endl;
|
||||
std::cout << GridLogIterative << std::setprecision(4)
|
||||
<< "ConjugateGradient: mp " << d << std::endl;
|
||||
std::cout << GridLogIterative << std::setprecision(4)
|
||||
<< "ConjugateGradient: mmp " << b << std::endl;
|
||||
std::cout << GridLogIterative << std::setprecision(4)
|
||||
<< "ConjugateGradient: cp,r " << cp << std::endl;
|
||||
std::cout << GridLogIterative << std::setprecision(4)
|
||||
<< "ConjugateGradient: p " << a << std::endl;
|
||||
std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradient: guess " << guess << std::endl;
|
||||
std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradient: src " << ssq << std::endl;
|
||||
std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradient: mp " << d << std::endl;
|
||||
std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradient: mmp " << b << std::endl;
|
||||
std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradient: cp,r " << cp << std::endl;
|
||||
std::cout << GridLogIterative << std::setprecision(4) << "ConjugateGradient: p " << a << std::endl;
|
||||
|
||||
RealD rsq = Tolerance * Tolerance * ssq;
|
||||
|
||||
@@ -99,8 +93,7 @@ class ConjugateGradient : public OperatorFunction<Field> {
|
||||
}
|
||||
|
||||
std::cout << GridLogIterative << std::setprecision(4)
|
||||
<< "ConjugateGradient: k=0 residual " << cp << " target " << rsq
|
||||
<< std::endl;
|
||||
<< "ConjugateGradient: k=0 residual " << cp << " target " << rsq << std::endl;
|
||||
|
||||
GridStopWatch LinalgTimer;
|
||||
GridStopWatch MatrixTimer;
|
||||
@@ -148,19 +141,20 @@ class ConjugateGradient : public OperatorFunction<Field> {
|
||||
RealD resnorm = sqrt(norm2(p));
|
||||
RealD true_residual = resnorm / srcnorm;
|
||||
|
||||
std::cout << GridLogMessage
|
||||
<< "ConjugateGradient: Converged on iteration " << k << std::endl;
|
||||
std::cout << GridLogMessage << "Computed residual " << sqrt(cp / ssq)
|
||||
<< " true residual " << true_residual << " target "
|
||||
<< Tolerance << std::endl;
|
||||
std::cout << GridLogMessage << "Time elapsed: Iterations "
|
||||
<< SolverTimer.Elapsed() << " Matrix "
|
||||
<< MatrixTimer.Elapsed() << " Linalg "
|
||||
<< LinalgTimer.Elapsed();
|
||||
std::cout << std::endl;
|
||||
std::cout << GridLogMessage << "ConjugateGradient Converged on iteration " << k << std::endl;
|
||||
std::cout << GridLogMessage << "\tComputed residual " << 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;
|
||||
|
||||
if (ErrorOnNoConverge) assert(true_residual / Tolerance < 10000.0);
|
||||
|
||||
IterationsToComplete = k;
|
||||
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -30,6 +30,7 @@ Author: paboyle <paboyle@ph.ed.ac.uk>
|
||||
#define GRID_IRL_H
|
||||
|
||||
#include <string.h> //memset
|
||||
|
||||
#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,
|
||||
@@ -37,8 +38,9 @@ void LAPACK_dstegr(char *jobz, char *range, int *n, double *d, double *e,
|
||||
double *work, int *lwork, int *iwork, int *liwork,
|
||||
int *info);
|
||||
#endif
|
||||
#include "DenseMatrix.h"
|
||||
#include "EigenSort.h"
|
||||
|
||||
#include <Grid/algorithms/densematrix/DenseMatrix.h>
|
||||
#include <Grid/algorithms/iterative/EigenSort.h>
|
||||
|
||||
namespace Grid {
|
||||
|
||||
@@ -1088,8 +1090,6 @@ static void Lock(DenseMatrix<T> &H, // Hess mtx
|
||||
int dfg,
|
||||
bool herm)
|
||||
{
|
||||
|
||||
|
||||
//ForceTridiagonal(H);
|
||||
|
||||
int M = H.dim;
|
||||
@@ -1121,7 +1121,6 @@ static void Lock(DenseMatrix<T> &H, // Hess mtx
|
||||
|
||||
AH = Hermitian(QQ)*AH;
|
||||
AH = AH*QQ;
|
||||
|
||||
|
||||
for(int i=con;i<M;i++){
|
||||
for(int j=con;j<M;j++){
|
||||
|
||||
@@ -1,453 +0,0 @@
|
||||
/*************************************************************************************
|
||||
|
||||
Grid physics library, www.github.com/paboyle/Grid
|
||||
|
||||
Source file: ./lib/algorithms/iterative/Matrix.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 MATRIX_H
|
||||
#define MATRIX_H
|
||||
|
||||
#include <cstdlib>
|
||||
#include <string>
|
||||
#include <cmath>
|
||||
#include <vector>
|
||||
#include <iostream>
|
||||
#include <iomanip>
|
||||
#include <complex>
|
||||
#include <typeinfo>
|
||||
#include <Grid/Grid.h>
|
||||
|
||||
|
||||
/** Sign function **/
|
||||
template <class T> T sign(T p){return ( p/abs(p) );}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////// Hijack STL containers for our wicked means /////////////////////////////////////////
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
template<class T> using Vector = Vector<T>;
|
||||
template<class T> using Matrix = Vector<Vector<T> >;
|
||||
|
||||
template<class T> void Resize(Vector<T > & vec, int N) { vec.resize(N); }
|
||||
|
||||
template<class T> void Resize(Matrix<T > & mat, int N, int M) {
|
||||
mat.resize(N);
|
||||
for(int i=0;i<N;i++){
|
||||
mat[i].resize(M);
|
||||
}
|
||||
}
|
||||
template<class T> void Size(Vector<T> & vec, int &N)
|
||||
{
|
||||
N= vec.size();
|
||||
}
|
||||
template<class T> void Size(Matrix<T> & mat, int &N,int &M)
|
||||
{
|
||||
N= mat.size();
|
||||
M= mat[0].size();
|
||||
}
|
||||
template<class T> void SizeSquare(Matrix<T> & mat, int &N)
|
||||
{
|
||||
int M; Size(mat,N,M);
|
||||
assert(N==M);
|
||||
}
|
||||
template<class T> void SizeSame(Matrix<T> & mat1,Matrix<T> &mat2, int &N1,int &M1)
|
||||
{
|
||||
int N2,M2;
|
||||
Size(mat1,N1,M1);
|
||||
Size(mat2,N2,M2);
|
||||
assert(N1==N2);
|
||||
assert(M1==M2);
|
||||
}
|
||||
|
||||
//*****************************************
|
||||
//* (Complex) Vector operations *
|
||||
//*****************************************
|
||||
|
||||
/**Conj of a Vector **/
|
||||
template <class T> Vector<T> conj(Vector<T> p){
|
||||
Vector<T> q(p.size());
|
||||
for(int i=0;i<p.size();i++){q[i] = conj(p[i]);}
|
||||
return q;
|
||||
}
|
||||
|
||||
/** Norm of a Vector**/
|
||||
template <class T> T norm(Vector<T> p){
|
||||
T sum = 0;
|
||||
for(int i=0;i<p.size();i++){sum = sum + p[i]*conj(p[i]);}
|
||||
return abs(sqrt(sum));
|
||||
}
|
||||
|
||||
/** Norm squared of a Vector **/
|
||||
template <class T> T norm2(Vector<T> p){
|
||||
T sum = 0;
|
||||
for(int i=0;i<p.size();i++){sum = sum + p[i]*conj(p[i]);}
|
||||
return abs((sum));
|
||||
}
|
||||
|
||||
/** Sum elements of a Vector **/
|
||||
template <class T> T trace(Vector<T> p){
|
||||
T sum = 0;
|
||||
for(int i=0;i<p.size();i++){sum = sum + p[i];}
|
||||
return sum;
|
||||
}
|
||||
|
||||
/** Fill a Vector with constant c **/
|
||||
template <class T> void Fill(Vector<T> &p, T c){
|
||||
for(int i=0;i<p.size();i++){p[i] = c;}
|
||||
}
|
||||
/** Normalize a Vector **/
|
||||
template <class T> void normalize(Vector<T> &p){
|
||||
T m = norm(p);
|
||||
if( abs(m) > 0.0) for(int i=0;i<p.size();i++){p[i] /= m;}
|
||||
}
|
||||
/** Vector by scalar **/
|
||||
template <class T, class U> Vector<T> times(Vector<T> p, U s){
|
||||
for(int i=0;i<p.size();i++){p[i] *= s;}
|
||||
return p;
|
||||
}
|
||||
template <class T, class U> Vector<T> times(U s, Vector<T> p){
|
||||
for(int i=0;i<p.size();i++){p[i] *= s;}
|
||||
return p;
|
||||
}
|
||||
/** inner product of a and b = conj(a) . b **/
|
||||
template <class T> T inner(Vector<T> a, Vector<T> b){
|
||||
T m = 0.;
|
||||
for(int i=0;i<a.size();i++){m = m + conj(a[i])*b[i];}
|
||||
return m;
|
||||
}
|
||||
/** sum of a and b = a + b **/
|
||||
template <class T> Vector<T> add(Vector<T> a, Vector<T> b){
|
||||
Vector<T> m(a.size());
|
||||
for(int i=0;i<a.size();i++){m[i] = a[i] + b[i];}
|
||||
return m;
|
||||
}
|
||||
/** sum of a and b = a - b **/
|
||||
template <class T> Vector<T> sub(Vector<T> a, Vector<T> b){
|
||||
Vector<T> m(a.size());
|
||||
for(int i=0;i<a.size();i++){m[i] = a[i] - b[i];}
|
||||
return m;
|
||||
}
|
||||
|
||||
/**
|
||||
*********************************
|
||||
* Matrices *
|
||||
*********************************
|
||||
**/
|
||||
|
||||
template<class T> void Fill(Matrix<T> & mat, T&val) {
|
||||
int N,M;
|
||||
Size(mat,N,M);
|
||||
for(int i=0;i<N;i++){
|
||||
for(int j=0;j<M;j++){
|
||||
mat[i][j] = val;
|
||||
}}
|
||||
}
|
||||
|
||||
/** Transpose of a matrix **/
|
||||
Matrix<T> Transpose(Matrix<T> & mat){
|
||||
int N,M;
|
||||
Size(mat,N,M);
|
||||
Matrix C; Resize(C,M,N);
|
||||
for(int i=0;i<M;i++){
|
||||
for(int j=0;j<N;j++){
|
||||
C[i][j] = mat[j][i];
|
||||
}}
|
||||
return C;
|
||||
}
|
||||
/** Set Matrix to unit matrix **/
|
||||
template<class T> void Unity(Matrix<T> &mat){
|
||||
int N; SizeSquare(mat,N);
|
||||
for(int i=0;i<N;i++){
|
||||
for(int j=0;j<N;j++){
|
||||
if ( i==j ) A[i][j] = 1;
|
||||
else A[i][j] = 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
/** Add C * I to matrix **/
|
||||
template<class T>
|
||||
void PlusUnit(Matrix<T> & A,T c){
|
||||
int dim; SizeSquare(A,dim);
|
||||
for(int i=0;i<dim;i++){A[i][i] = A[i][i] + c;}
|
||||
}
|
||||
|
||||
/** return the Hermitian conjugate of matrix **/
|
||||
Matrix<T> HermitianConj(Matrix<T> &mat){
|
||||
|
||||
int dim; SizeSquare(mat,dim);
|
||||
|
||||
Matrix<T> C; Resize(C,dim,dim);
|
||||
|
||||
for(int i=0;i<dim;i++){
|
||||
for(int j=0;j<dim;j++){
|
||||
C[i][j] = conj(mat[j][i]);
|
||||
}
|
||||
}
|
||||
return C;
|
||||
}
|
||||
|
||||
/** return diagonal entries as a Vector **/
|
||||
Vector<T> diag(Matrix<T> &A)
|
||||
{
|
||||
int dim; SizeSquare(A,dim);
|
||||
Vector<T> d; Resize(d,dim);
|
||||
|
||||
for(int i=0;i<dim;i++){
|
||||
d[i] = A[i][i];
|
||||
}
|
||||
return d;
|
||||
}
|
||||
|
||||
/** Left multiply by a Vector **/
|
||||
Vector<T> operator *(Vector<T> &B,Matrix<T> &A)
|
||||
{
|
||||
int K,M,N;
|
||||
Size(B,K);
|
||||
Size(A,M,N);
|
||||
assert(K==M);
|
||||
|
||||
Vector<T> C; Resize(C,N);
|
||||
|
||||
for(int j=0;j<N;j++){
|
||||
T sum = 0.0;
|
||||
for(int i=0;i<M;i++){
|
||||
sum += B[i] * A[i][j];
|
||||
}
|
||||
C[j] = sum;
|
||||
}
|
||||
return C;
|
||||
}
|
||||
|
||||
/** return 1/diagonal entries as a Vector **/
|
||||
Vector<T> inv_diag(Matrix<T> & A){
|
||||
int dim; SizeSquare(A,dim);
|
||||
Vector<T> d; Resize(d,dim);
|
||||
for(int i=0;i<dim;i++){
|
||||
d[i] = 1.0/A[i][i];
|
||||
}
|
||||
return d;
|
||||
}
|
||||
/** Matrix Addition **/
|
||||
inline Matrix<T> operator + (Matrix<T> &A,Matrix<T> &B)
|
||||
{
|
||||
int N,M ; SizeSame(A,B,N,M);
|
||||
Matrix C; Resize(C,N,M);
|
||||
for(int i=0;i<N;i++){
|
||||
for(int j=0;j<M;j++){
|
||||
C[i][j] = A[i][j] + B[i][j];
|
||||
}
|
||||
}
|
||||
return C;
|
||||
}
|
||||
/** Matrix Subtraction **/
|
||||
inline Matrix<T> operator- (Matrix<T> & A,Matrix<T> &B){
|
||||
int N,M ; SizeSame(A,B,N,M);
|
||||
Matrix C; Resize(C,N,M);
|
||||
for(int i=0;i<N;i++){
|
||||
for(int j=0;j<M;j++){
|
||||
C[i][j] = A[i][j] - B[i][j];
|
||||
}}
|
||||
return C;
|
||||
}
|
||||
|
||||
/** Matrix scalar multiplication **/
|
||||
inline Matrix<T> operator* (Matrix<T> & A,T c){
|
||||
int N,M; Size(A,N,M);
|
||||
Matrix C; Resize(C,N,M);
|
||||
for(int i=0;i<N;i++){
|
||||
for(int j=0;j<M;j++){
|
||||
C[i][j] = A[i][j]*c;
|
||||
}}
|
||||
return C;
|
||||
}
|
||||
/** Matrix Matrix multiplication **/
|
||||
inline Matrix<T> operator* (Matrix<T> &A,Matrix<T> &B){
|
||||
int K,L,N,M;
|
||||
Size(A,K,L);
|
||||
Size(B,N,M); assert(L==N);
|
||||
Matrix C; Resize(C,K,M);
|
||||
|
||||
for(int i=0;i<K;i++){
|
||||
for(int j=0;j<M;j++){
|
||||
T sum = 0.0;
|
||||
for(int k=0;k<N;k++) sum += A[i][k]*B[k][j];
|
||||
C[i][j] =sum;
|
||||
}
|
||||
}
|
||||
return C;
|
||||
}
|
||||
/** Matrix Vector multiplication **/
|
||||
inline Vector<T> operator* (Matrix<T> &A,Vector<T> &B){
|
||||
int M,N,K;
|
||||
Size(A,N,M);
|
||||
Size(B,K); assert(K==M);
|
||||
Vector<T> C; Resize(C,N);
|
||||
for(int i=0;i<N;i++){
|
||||
T sum = 0.0;
|
||||
for(int j=0;j<M;j++) sum += A[i][j]*B[j];
|
||||
C[i] = sum;
|
||||
}
|
||||
return C;
|
||||
}
|
||||
|
||||
/** Some version of Matrix norm **/
|
||||
/*
|
||||
inline T Norm(){ // this is not a usual L2 norm
|
||||
T norm = 0;
|
||||
for(int i=0;i<dim;i++){
|
||||
for(int j=0;j<dim;j++){
|
||||
norm += abs(A[i][j]);
|
||||
}}
|
||||
return norm;
|
||||
}
|
||||
*/
|
||||
|
||||
/** Some version of Matrix norm **/
|
||||
template<class T> T LargestDiag(Matrix<T> &A)
|
||||
{
|
||||
int dim ; SizeSquare(A,dim);
|
||||
|
||||
T ld = abs(A[0][0]);
|
||||
for(int i=1;i<dim;i++){
|
||||
T cf = abs(A[i][i]);
|
||||
if(abs(cf) > abs(ld) ){ld = cf;}
|
||||
}
|
||||
return ld;
|
||||
}
|
||||
|
||||
/** Look for entries on the leading subdiagonal that are smaller than 'small' **/
|
||||
template <class T,class U> int Chop_subdiag(Matrix<T> &A,T norm, int offset, U small)
|
||||
{
|
||||
int dim; SizeSquare(A,dim);
|
||||
for(int l = dim - 1 - offset; l >= 1; l--) {
|
||||
if((U)abs(A[l][l - 1]) < (U)small) {
|
||||
A[l][l-1]=(U)0.0;
|
||||
return l;
|
||||
}
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
/** Look for entries on the leading subdiagonal that are smaller than 'small' **/
|
||||
template <class T,class U> int Chop_symm_subdiag(Matrix<T> & A,T norm, int offset, U small)
|
||||
{
|
||||
int dim; SizeSquare(A,dim);
|
||||
for(int l = dim - 1 - offset; l >= 1; l--) {
|
||||
if((U)abs(A[l][l - 1]) < (U)small) {
|
||||
A[l][l - 1] = (U)0.0;
|
||||
A[l - 1][l] = (U)0.0;
|
||||
return l;
|
||||
}
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
/**Assign a submatrix to a larger one**/
|
||||
template<class T>
|
||||
void AssignSubMtx(Matrix<T> & A,int row_st, int row_end, int col_st, int col_end, Matrix<T> &S)
|
||||
{
|
||||
for(int i = row_st; i<row_end; i++){
|
||||
for(int j = col_st; j<col_end; j++){
|
||||
A[i][j] = S[i - row_st][j - col_st];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**Get a square submatrix**/
|
||||
template <class T>
|
||||
Matrix<T> GetSubMtx(Matrix<T> &A,int row_st, int row_end, int col_st, int col_end)
|
||||
{
|
||||
Matrix<T> H; Resize(row_end - row_st,col_end-col_st);
|
||||
|
||||
for(int i = row_st; i<row_end; i++){
|
||||
for(int j = col_st; j<col_end; j++){
|
||||
H[i-row_st][j-col_st]=A[i][j];
|
||||
}}
|
||||
return H;
|
||||
}
|
||||
|
||||
/**Assign a submatrix to a larger one NB remember Vector Vectors are transposes of the matricies they represent**/
|
||||
template<class T>
|
||||
void AssignSubMtx(Matrix<T> & A,int row_st, int row_end, int col_st, int col_end, Matrix<T> &S)
|
||||
{
|
||||
for(int i = row_st; i<row_end; i++){
|
||||
for(int j = col_st; j<col_end; j++){
|
||||
A[i][j] = S[i - row_st][j - col_st];
|
||||
}}
|
||||
}
|
||||
|
||||
/** compute b_i A_ij b_j **/ // surprised no Conj
|
||||
template<class T> T proj(Matrix<T> A, Vector<T> B){
|
||||
int dim; SizeSquare(A,dim);
|
||||
int dimB; Size(B,dimB);
|
||||
assert(dimB==dim);
|
||||
T C = 0;
|
||||
for(int i=0;i<dim;i++){
|
||||
T sum = 0.0;
|
||||
for(int j=0;j<dim;j++){
|
||||
sum += A[i][j]*B[j];
|
||||
}
|
||||
C += B[i]*sum; // No conj?
|
||||
}
|
||||
return C;
|
||||
}
|
||||
|
||||
|
||||
/*
|
||||
*************************************************************
|
||||
*
|
||||
* Matrix Vector products
|
||||
*
|
||||
*************************************************************
|
||||
*/
|
||||
// Instead make a linop and call my CG;
|
||||
|
||||
/// q -> q Q
|
||||
template <class T,class Fermion> void times(Vector<Fermion> &q, Matrix<T> &Q)
|
||||
{
|
||||
int M; SizeSquare(Q,M);
|
||||
int N; Size(q,N);
|
||||
assert(M==N);
|
||||
|
||||
times(q,Q,N);
|
||||
}
|
||||
|
||||
/// q -> q Q
|
||||
template <class T> void times(multi1d<LatticeFermion> &q, Matrix<T> &Q, int N)
|
||||
{
|
||||
GridBase *grid = q[0]._grid;
|
||||
int M; SizeSquare(Q,M);
|
||||
int K; Size(q,K);
|
||||
assert(N<M);
|
||||
assert(N<K);
|
||||
Vector<Fermion> S(N,grid );
|
||||
for(int j=0;j<N;j++){
|
||||
S[j] = zero;
|
||||
for(int k=0;k<N;k++){
|
||||
S[j] = S[j] + q[k]* Q[k][j];
|
||||
}
|
||||
}
|
||||
for(int j=0;j<q.size();j++){
|
||||
q[j] = S[j];
|
||||
}
|
||||
}
|
||||
#endif
|
||||
@@ -1,75 +0,0 @@
|
||||
/*************************************************************************************
|
||||
|
||||
Grid physics library, www.github.com/paboyle/Grid
|
||||
|
||||
Source file: ./lib/algorithms/iterative/MatrixUtils.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_MATRIX_UTILS_H
|
||||
#define GRID_MATRIX_UTILS_H
|
||||
|
||||
namespace Grid {
|
||||
|
||||
namespace MatrixUtils {
|
||||
|
||||
template<class T> inline void Size(Matrix<T>& A,int &N,int &M){
|
||||
N=A.size(); assert(N>0);
|
||||
M=A[0].size();
|
||||
for(int i=0;i<N;i++){
|
||||
assert(A[i].size()==M);
|
||||
}
|
||||
}
|
||||
|
||||
template<class T> inline void SizeSquare(Matrix<T>& A,int &N)
|
||||
{
|
||||
int M;
|
||||
Size(A,N,M);
|
||||
assert(N==M);
|
||||
}
|
||||
|
||||
template<class T> inline void Fill(Matrix<T>& A,T & val)
|
||||
{
|
||||
int N,M;
|
||||
Size(A,N,M);
|
||||
for(int i=0;i<N;i++){
|
||||
for(int j=0;j<M;j++){
|
||||
A[i][j]=val;
|
||||
}}
|
||||
}
|
||||
template<class T> inline void Diagonal(Matrix<T>& A,T & val)
|
||||
{
|
||||
int N;
|
||||
SizeSquare(A,N);
|
||||
for(int i=0;i<N;i++){
|
||||
A[i][i]=val;
|
||||
}
|
||||
}
|
||||
template<class T> inline void Identity(Matrix<T>& A)
|
||||
{
|
||||
Fill(A,0.0);
|
||||
Diagonal(A,1.0);
|
||||
}
|
||||
|
||||
};
|
||||
}
|
||||
#endif
|
||||
@@ -1,15 +0,0 @@
|
||||
- ConjugateGradientMultiShift
|
||||
- MCR
|
||||
|
||||
- Potentially Useful Boost libraries
|
||||
|
||||
- MultiArray
|
||||
- Aligned allocator; memory pool
|
||||
- Remez -- Mike or Boost?
|
||||
- Multiprecision
|
||||
- quaternians
|
||||
- Tokenize
|
||||
- Serialization
|
||||
- Regex
|
||||
- Proto (ET)
|
||||
- uBlas
|
||||
@@ -1,122 +0,0 @@
|
||||
#include <math.h>
|
||||
#include <stdlib.h>
|
||||
#include <vector>
|
||||
|
||||
struct Bisection {
|
||||
|
||||
static void get_eig2(int row_num,std::vector<RealD> &ALPHA,std::vector<RealD> &BETA, std::vector<RealD> & eig)
|
||||
{
|
||||
int i,j;
|
||||
std::vector<RealD> evec1(row_num+3);
|
||||
std::vector<RealD> evec2(row_num+3);
|
||||
RealD eps2;
|
||||
ALPHA[1]=0.;
|
||||
BETHA[1]=0.;
|
||||
for(i=0;i<row_num-1;i++) {
|
||||
ALPHA[i+1] = A[i*(row_num+1)].real();
|
||||
BETHA[i+2] = A[i*(row_num+1)+1].real();
|
||||
}
|
||||
ALPHA[row_num] = A[(row_num-1)*(row_num+1)].real();
|
||||
bisec(ALPHA,BETHA,row_num,1,row_num,1e-10,1e-10,evec1,eps2);
|
||||
bisec(ALPHA,BETHA,row_num,1,row_num,1e-16,1e-16,evec2,eps2);
|
||||
|
||||
// Do we really need to sort here?
|
||||
int begin=1;
|
||||
int end = row_num;
|
||||
int swapped=1;
|
||||
while(swapped) {
|
||||
swapped=0;
|
||||
for(i=begin;i<end;i++){
|
||||
if(mag(evec2[i])>mag(evec2[i+1])) {
|
||||
swap(evec2+i,evec2+i+1);
|
||||
swapped=1;
|
||||
}
|
||||
}
|
||||
end--;
|
||||
for(i=end-1;i>=begin;i--){
|
||||
if(mag(evec2[i])>mag(evec2[i+1])) {
|
||||
swap(evec2+i,evec2+i+1);
|
||||
swapped=1;
|
||||
}
|
||||
}
|
||||
begin++;
|
||||
}
|
||||
|
||||
for(i=0;i<row_num;i++){
|
||||
for(j=0;j<row_num;j++) {
|
||||
if(i==j) H[i*row_num+j]=evec2[i+1];
|
||||
else H[i*row_num+j]=0.;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void bisec(std::vector<RealD> &c,
|
||||
std::vector<RealD> &b,
|
||||
int n,
|
||||
int m1,
|
||||
int m2,
|
||||
RealD eps1,
|
||||
RealD relfeh,
|
||||
std::vector<RealD> &x,
|
||||
RealD &eps2)
|
||||
{
|
||||
std::vector<RealD> wu(n+2);
|
||||
|
||||
RealD h,q,x1,xu,x0,xmin,xmax;
|
||||
int i,a,k;
|
||||
|
||||
b[1]=0.0;
|
||||
xmin=c[n]-fabs(b[n]);
|
||||
xmax=c[n]+fabs(b[n]);
|
||||
for(i=1;i<n;i++){
|
||||
h=fabs(b[i])+fabs(b[i+1]);
|
||||
if(c[i]+h>xmax) xmax= c[i]+h;
|
||||
if(c[i]-h<xmin) xmin= c[i]-h;
|
||||
}
|
||||
xmax *=2.;
|
||||
|
||||
eps2=relfeh*((xmin+xmax)>0.0 ? xmax : -xmin);
|
||||
if(eps1<=0.0) eps1=eps2;
|
||||
eps2=0.5*eps1+7.0*(eps2);
|
||||
x0=xmax;
|
||||
for(i=m1;i<=m2;i++){
|
||||
x[i]=xmax;
|
||||
wu[i]=xmin;
|
||||
}
|
||||
|
||||
for(k=m2;k>=m1;k--){
|
||||
xu=xmin;
|
||||
i=k;
|
||||
do{
|
||||
if(xu<wu[i]){
|
||||
xu=wu[i];
|
||||
i=m1-1;
|
||||
}
|
||||
i--;
|
||||
}while(i>=m1);
|
||||
if(x0>x[k]) x0=x[k];
|
||||
while((x0-xu)>2*relfeh*(fabs(xu)+fabs(x0))+eps1){
|
||||
x1=(xu+x0)/2;
|
||||
|
||||
a=0;
|
||||
q=1.0;
|
||||
for(i=1;i<=n;i++){
|
||||
q=c[i]-x1-((q!=0.0)? b[i]*b[i]/q:fabs(b[i])/relfeh);
|
||||
if(q<0) a++;
|
||||
}
|
||||
// printf("x1=%e a=%d\n",x1,a);
|
||||
if(a<k){
|
||||
if(a<m1){
|
||||
xu=x1;
|
||||
wu[m1]=x1;
|
||||
}else {
|
||||
xu=x1;
|
||||
wu[a+1]=x1;
|
||||
if(x[a]>x1) x[a]=x1;
|
||||
}
|
||||
}else x0=x1;
|
||||
}
|
||||
x[k]=(x0+xu)/2;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1 +0,0 @@
|
||||
|
||||
Reference in New Issue
Block a user