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/*
ARPACK++ v1.2 2/20/2000
c++ interface to ARPACK code.
MODULE LSymSol.h
Template functions that exemplify how to print information
about symmetric standard and generalized eigenvalue problems.
ARPACK Authors
Richard Lehoucq
Danny Sorensen
Chao Yang
Dept. of Computational & Applied Mathematics
Rice University
Houston, Texas
*/
#ifndef LSYMSOL_H
#define LSYMSOL_H
#include <math.h>
#include "blas1c.h"
#include "lapackc.h"
#ifdef ARLSMAT_H
#include "arlssym.h"
#include "arlgsym.h"
#elif defined ARUSMAT_H
#include "arussym.h"
#include "arugsym.h"
#elif defined ARDSMAT_H
#include "ardssym.h"
#include "ardgsym.h"
#else
#include "arbssym.h"
#include "arbgsym.h"
#endif
template<class ARMATRIX, class ARFLOAT>
void Solution(ARMATRIX &A, ARluSymStdEig<ARFLOAT> &Prob)
/*
Prints eigenvalues and eigenvectors of symmetric eigen-problems
on standard "std::cout" stream.
*/
{
int i, n, nconv, mode;
ARFLOAT *Ax;
ARFLOAT *ResNorm;
n = Prob.GetN();
nconv = Prob.ConvergedEigenvalues();
mode = Prob.GetMode();
std::cout << std::endl << std::endl << "Testing ARPACK++ class ARluSymStdEig \n";
std::cout << "Real symmetric eigenvalue problem: A*x - lambda*x" << std::endl;
switch (mode) {
case 1:
std::cout << "Regular mode" << std::endl;
break;
case 3:
std::cout << "Shift and invert mode" << std::endl;
}
std::cout << std::endl;
std::cout << "Dimension of the system : " << n << std::endl;
std::cout << "Number of 'requested' eigenvalues : " << Prob.GetNev() << std::endl;
std::cout << "Number of 'converged' eigenvalues : " << nconv << std::endl;
std::cout << "Number of Arnoldi vectors generated: " << Prob.GetNcv() << std::endl;
std::cout << "Number of iterations taken : " << Prob.GetIter() << std::endl;
std::cout << std::endl;
if (Prob.EigenvaluesFound()) {
// Printing eigenvalues.
std::cout << "Eigenvalues:" << std::endl;
for (i=0; i<nconv; i++) {
std::cout << " lambda[" << (i+1) << "]: " << Prob.Eigenvalue(i) << std::endl;
}
std::cout << std::endl;
}
if (Prob.EigenvectorsFound()) {
// Printing the residual norm || A*x - lambda*x ||
// for the nconv accurately computed eigenvectors.
Ax = new ARFLOAT[n];
ResNorm = new ARFLOAT[nconv+1];
for (i=0; i<nconv; i++) {
A.MultMv(Prob.RawEigenvector(i), Ax);
axpy(n, -Prob.Eigenvalue(i), Prob.RawEigenvector(i), 1, Ax, 1);
ResNorm[i] = nrm2(n, Ax, 1)/fabs(Prob.Eigenvalue(i));
}
for (i=0; i<nconv; i++) {
std::cout << "||A*x(" << (i+1) << ") - lambda(" << (i+1);
std::cout << ")*x(" << (i+1) << ")||: " << ResNorm[i] << "\n";
}
std::cout << "\n";
delete[] Ax;
delete[] ResNorm;
}
} // Solution
template<class MATRA, class MATRB, class ARFLOAT>
void Solution(MATRA &A, MATRB &B, ARluSymGenEig<ARFLOAT> &Prob)
/*
Prints eigenvalues and eigenvectors of symmetric generalized
eigen-problems on standard "std::cout" stream.
*/
{
int i, n, nconv, mode;
ARFLOAT *Ax, *Bx, *ResNorm;
n = Prob.GetN();
nconv = Prob.ConvergedEigenvalues();
mode = Prob.GetMode();
std::cout << std::endl << std::endl << "Testing ARPACK++ class ARluSymGenEig \n";
std::cout << "Real symmetric generalized eigenvalue problem: A*x - lambda*B*x";
std::cout << std::endl;
switch (mode) {
case 2:
std::cout << "Regular mode" << std::endl;
break;
case 3:
std::cout << "Shift and invert mode" << std::endl;
break;
case 4:
std::cout << "Buckling mode" << std::endl;
break;
case 5:
std::cout << "Cayley mode" << std::endl;
}
std::cout << std::endl;
std::cout << "Dimension of the system : " << n << std::endl;
std::cout << "Number of 'requested' eigenvalues : " << Prob.GetNev() << std::endl;
std::cout << "Number of 'converged' eigenvalues : " << nconv << std::endl;
std::cout << "Number of Arnoldi vectors generated: " << Prob.GetNcv() << std::endl;
std::cout << "Number of iterations taken : " << Prob.GetIter() << std::endl;
std::cout << std::endl;
if (Prob.EigenvaluesFound()) {
// Printing eigenvalues.
std::cout << "Eigenvalues:" << std::endl;
for (i=0; i<nconv; i++) {
std::cout << " lambda[" << (i+1) << "]: " << Prob.Eigenvalue(i) << std::endl;
}
std::cout << std::endl;
}
if (Prob.EigenvectorsFound()) {
// Printing the residual norm || A*x - lambda*B*x ||
// for the nconv accurately computed eigenvectors.
Ax = new ARFLOAT[n];
Bx = new ARFLOAT[n];
ResNorm = new ARFLOAT[nconv+1];
for (i=0; i<nconv; i++) {
A.MultMv(Prob.RawEigenvector(i), Ax);
B.MultMv(Prob.RawEigenvector(i), Bx);
axpy(n, -Prob.Eigenvalue(i), Bx, 1, Ax, 1);
ResNorm[i] = nrm2(n, Ax, 1)/fabs(Prob.Eigenvalue(i));
}
for (i=0; i<nconv; i++) {
std::cout << "||A*x(" << i << ") - lambda(" << i;
std::cout << ")*B*x(" << i << ")||: " << ResNorm[i] << "\n";
}
std::cout << "\n";
delete[] Ax;
delete[] Bx;
delete[] ResNorm;
}
} // Solution
#endif // LSYMSOL_H
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