NICE_VisLearning/math/algebra_trlan/Eigenproblem.h

#ifndef EigenproblemINCLUDE
#define EigenproblemINCLUDE

#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <vector>
#include <string.h>


typedef struct{
	uint 	x;
	uint 	y;
	double 	val;
} element;


namespace OBJREC {

/** depreceated version: Real Symmetric Eigenproblem - no parallel mode
 the eigenpairs returned by the method solve are ordered increasingly */
class Eigenproblem{
      
private:

public:

	///performs matrix-vector multiplication with sparse matrix this->mat
	void sparseMult(double* xin, double* yout, int ldy);

	std::vector<element> mat;

	Eigenproblem(){};
	~Eigenproblem(){};

	/** Solves the user defined eigenproblem with the following parameters:
           mat             -   Sparse matrix
           mat_size        -   matrix size, i.e. dim(mat)=mat_size x mat_size
           evals           -   std::vector of eigenvalues
           evecs           -   std::vector of k eigenvectors
           k               -   number of eigenvalues/eigenvectors to compute
           magnitude       -   Specifies the nature of the eigenproblem: 
                               find eigenpair with respect to the k largest (magnitude>0) 
                               or smallest (magnitude<0) eigenvalues
           restarting_...  -   restarting scheme of TRLAN
           max_mult        -   maximum number of matrix-vector multiplications to perform
           tolerance       -   stop criterion (abs. resudual |A*x-lambda*x|?)
           mult_flops       -  number of flops per matrix multiplication (used for statistics calculation)
	*/
	void solve(	std::vector<element> mat, int mat_size, std::vector<double> &evals, 
                        std::vector< std::vector<double> > &evecs, int k=1, int magnitude=1,
                        int restarting_scheme=1,double tolerance=1e-8, int max_mult=-1,
                        int mult_flops=-1);
};


} // namespace

#endif