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Hello, I need to get the most 1 smallest algrebraic eigen value of a big size symmetric matrix G with a level about 160000*160000 to see if G is positive definite . I try the solver SymEigsSolver but the result shows me failed. Here is the code fragment.
SparseGenMatProd<double> op(G);
// Construct eigen solver object, requesting the largest three eigenvalues
SymEigsSolver<SparseGenMatProd<double>> eigs(op, 4, 6);
// Initialize and compute
eigs.init();
int nconv = eigs.compute(SortRule::SmallestAlge);
And If G is positve definete , I have another symmetric matrix C (but may be singular ) with the same size as G, I need to solve the largest k algebraic generalized eigen value $\lambda$ for the problem $Cx = \lambda Gx$. Without validtation the property of G , I have try to use SymGEigsSolver (Cholesky mode and RegularInverse), SymGEigsShiftSolver, but all failed or too long to be waited. Here are the code fragments, I wanna if there are something wrong. Since the size of the matrix is too large, I really want to konw how to improve the calculation speed, thanks for answering!
Hello, I need to get the most 1 smallest algrebraic eigen value of a big size symmetric matrix G with a level about 160000*160000 to see if G is positive definite . I try the solver SymEigsSolver but the result shows me failed. Here is the code fragment.
And If G is positve definete , I have another symmetric matrix C (but may be singular ) with the same size as G, I need to solve the largest k algebraic generalized eigen value$\lambda$ for the problem $Cx = \lambda Gx$ . Without validtation the property of G , I have try to use SymGEigsSolver (Cholesky mode and RegularInverse), SymGEigsShiftSolver, but all failed or too long to be waited. Here are the code fragments, I wanna if there are something wrong. Since the size of the matrix is too large, I really want to konw how to improve the calculation speed, thanks for answering!
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