Spectra::SymGEigsShiftSolver< OpType, BOpType, GEigsMode::Buckling > Class Template Reference

#include <Spectra/SymGEigsShiftSolver.h>

Inheritance diagram for Spectra::SymGEigsShiftSolver< OpType, BOpType, GEigsMode::Buckling >:

Public Member Functions
	SymGEigsShiftSolver (OpType &op, BOpType &Bop, Index nev, Index ncv, const Scalar &sigma)
Public Member Functions inherited from Spectra::HermEigsBase< SymGEigsBucklingOp< OpType, BOpType >, BOpType >
void	init (const Scalar *init_resid)
Index	compute (SortRule selection=SortRule::LargestMagn, Index maxit=1000, RealScalar tol=1e-10, SortRule sorting=SortRule::LargestAlge)
CompInfo	info () const
Index	num_iterations () const
Index	num_operations () const
RealVector	eigenvalues () const
virtual Matrix	eigenvectors (Index nvec) const

Detailed Description

template<typename OpType, typename BOpType>
class Spectra::SymGEigsShiftSolver< OpType, BOpType, GEigsMode::Buckling >

This class implements the generalized eigen solver for real symmetric matrices in the buckling mode. The original problem is to solve Kx=\lambda K_G x, where K is positive definite and K_G is symmetric. The transformed problem is (K-\sigma K_G)^{-1}Kx=\nu x, where \nu=\lambda/(\lambda-\sigma), and \sigma is a user-specified shift.

This solver requires two matrix operation objects: one to compute y=(K-\sigma K_G)^{-1}x for any vector v, and one for the matrix multiplication Kv.

If K and K_G are stored as Eigen matrices, then the first operation object can be created using the SymShiftInvert class, and the second one can be created using the DenseSymMatProd or SparseSymMatProd classes. If the users need to define their own operation classes, then they should implement all the public member functions as in those built-in classes.

Template Parameters

OpType	The type of the first operation object. Users could either use the wrapper class SymShiftInvert, or define their own that implements the type definition Scalar and all the public member functions as in SymShiftInvert.
BOpType	The name of the matrix operation class for K. Users could either use the wrapper classes such as DenseSymMatProd and SparseSymMatProd, or define their own that implements all the public member functions as in DenseSymMatProd.
Mode	Mode of the generalized eigen solver. In this solver it is Spectra::GEigsMode::Buckling.

Below is an example that demonstrates the usage of this class.

#include <Eigen/Core>
#include <Eigen/SparseCore>
#include <Spectra/SymGEigsShiftSolver.h>
#include <Spectra/MatOp/SymShiftInvert.h>
#include <Spectra/MatOp/SparseSymMatProd.h>
#include <iostream>
 
using namespace Spectra;
 
int main()
{
    // We are going to solve the generalized eigenvalue problem
    //     K * x = lambda * KG * x,
    // where K is positive definite, and KG is symmetric
    const int n = 100;
 
    // Define the K matrix, a tridiagonal matrix with 2 on the diagonal
    // and 1 on the subdiagonals
    Eigen::SparseMatrix<double> K(n, n);
    K.reserve(Eigen::VectorXi::Constant(n, 3));
    for (int i = 0; i < n; i++)
    {
        K.insert(i, i) = 2.0;
        if (i > 0)
            K.insert(i - 1, i) = 1.0;
        if (i < n - 1)
            K.insert(i + 1, i) = 1.0;
    }
 
    // Define the KG matrix
    Eigen::MatrixXd M = Eigen::MatrixXd::Random(n, n);
    Eigen::MatrixXd KG = M + M.transpose();
 
    // Construct matrix operation objects using the wrapper classes
    // K is sparse, KG is dense
    using OpType = SymShiftInvert<double, Eigen::Sparse, Eigen::Dense>;
    using BOpType = SparseSymMatProd<double>;
    OpType op(K, KG);
    BOpType Bop(K);
 
    // Construct generalized eigen solver object, seeking three generalized
    // eigenvalues that are closest to and larger than 1.0. This is equivalent to
    // specifying a shift sigma = 1.0 combined with the SortRule::LargestAlge
    // selection rule
    SymGEigsShiftSolver<OpType, BOpType, GEigsMode::Buckling>
        geigs(op, Bop, 3, 6, 1.0);
 
    // Initialize and compute
    geigs.init();
    int nconv = geigs.compute(SortRule::LargestAlge);
 
    // Retrieve results
    Eigen::VectorXd evalues;
    Eigen::MatrixXd evecs;
    if (geigs.info() == CompInfo::Successful)
    {
        evalues = geigs.eigenvalues();
        evecs = geigs.eigenvectors();
    }
 
    std::cout << "Number of converged generalized eigenvalues: " << nconv << std::endl;
    std::cout << "Generalized eigenvalues found:\n" << evalues << std::endl;
    std::cout << "Generalized eigenvectors found:\n" << evecs.topRows(10) << std::endl;
 
    return 0;
}

Definition at line 307 of file SymGEigsShiftSolver.h.

Constructor & Destructor Documentation

SymGEigsShiftSolver()

template<typename OpType, typename BOpType>

Spectra::SymGEigsShiftSolver< OpType, BOpType, GEigsMode::Buckling >::SymGEigsShiftSolver ( OpType & op, BOpType & Bop, Index nev, Index ncv, const Scalar & sigma )

inline

Constructor to create a solver object.

Parameters

op	The matrix operation object that computes y=(K-\sigma K_G)^{-1}v for any vector v. Users could either create the object from the wrapper class SymShiftInvert, or define their own that implements all the public members as in SymShiftInvert.
Bop	The K matrix operation object that implements the matrix-vector multiplication Kv. Users could either create the object from the wrapper classes such as DenseSymMatProd and SparseSymMatProd, or define their own that implements all the public member functions as in DenseSymMatProd. K needs to be positive definite.
nev	Number of eigenvalues requested. This should satisfy 1\le nev \le n-1, where n is the size of matrix.
ncv	Parameter that controls the convergence speed of the algorithm. Typically a larger ncv means faster convergence, but it may also result in greater memory use and more matrix operations in each iteration. This parameter must satisfy nev < ncv \le n, and is advised to take ncv \ge 2\cdot nev.
sigma	The value of the shift.

Definition at line 363 of file SymGEigsShiftSolver.h.

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The documentation for this class was generated from the following file:

• Spectra/SymGEigsShiftSolver.h