📄 dnsymgsc.cc
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/* ARPACK++ v1.0 8/1/1997 c++ interface to ARPACK code. MODULE DNSymGSC.cc. Example program that illustrates how to solve a real nonsymmetric dense generalized eigenvalue problem in complex shift and invert mode using the ARluNonSymGenEig class. 1) Problem description: In this example we try to solve A*x = B*x*lambda in shift and inverse mode, where A is a block tridiagonal matrix and B is a tridiagonal matrix. Each diagonal block of A is a tridiagonal matrix with 4 on the main diagonal, -1-rho*h/2 on the subdiagonal and -1+rho*h/2 on the superdiagonal. Each subdiagonal block of A is an identity matrix. B has 4 on the main diagonal and 1 on the other two diagonals. The shift sigma is a complex number. 2) Data structure used to represent matrices A and B: Although A and B are very sparse in this example, they are stored here as dense matrices (by columns). 3) Library called by this example: The LAPACK package is called by ARluNonSymGenEig to solve some linear systems involving (A-sigma*B). This is needed to implement the shift and invert strategy. 4) Included header files: File Contents ----------- ------------------------------------------- dnmatrxd.h DenseMatrixD, a function that generates matrix A. dnmatrxe.h DenseMatrixE, a function that generates matrix B. ardnsmat.h The ARdsNonSymMatrix class definition. ardgnsym.h The ARluNonSymGenEig class definition. lnsymsol.h The Solution function. 5) ARPACK Authors: Richard Lehoucq Kristyn Maschhoff Danny Sorensen Chao Yang Dept. of Computational & Applied Mathematics Rice University Houston, Texas*/#include "dnmatrxd.h"#include "dnmatrxe.h"#include "ardnsmat.h"#include "ardgnsym.h"#include "lnsymsol.h"main(){ // Defining variables; int nx; int n; // Dimension of the problem. double rho; // Parameter used to define A. double* valA; // pointer to an array that stores the elements of A. double* valB; // pointer to an array that stores the elements of B. // Creating matrices A and B. nx = 10; rho = 100.0; DenseMatrixD(nx, rho, n, valA); ARdsNonSymMatrix<double> A(n, valA); DenseMatrixE(n, valB); ARdsNonSymMatrix<double> B(n, valB); // Defining what we need: the four eigenvectors nearest to 0.4+0.6I. ARluNonSymGenEig<double> dprob(4L, A, B, 'I', 0.4, 0.6); // Finding eigenvalues and eigenvectors. dprob.FindEigenvectors(); // Printing solution. Solution(A, B, dprob);} // main.
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