[EN] Large-scale polynomial eigenvalue problems can be solved by Krylov methods operating on an equivalent linear eigenproblem (linearization) of size d center dot n where d is the polynomial degree and n is the problem ...

[ES] En muchas aplicaciones, aparece la necesidad de calcular para una matriz A una cierta función f(A), como puede ser la exponencial, el logaritmo, la raíz cuadrada, la función signo u otras. En algunas de estas aplicaciones, ...

[EN] This short article describes the main newfeatures added to SLEPc, the Scalable Library for Eigenvalue Problem Computations, in the past two and a half years, corresponding to five release versions. The main novelty ...

[EN] SLEPc is a parallel library for the solution of various types of large-scale eigenvalue problems. Over the past few years, we have been developing a module within SLEPc, called NEP, that is intended for solving nonlinear ...

Polynomial eigenvalue problems are often found in scientific computing applications. When the coefficient matrices of the polynomial are large and sparse, usually only a few eigenpairs are required and projection methods ...

We investigate how to adapt the Q-Arnoldi method for the case of symmetric quadratic eigenvalue problems, that is, we are interested in computing a few eigenpairs of with M, C, K symmetric matrices. This problem has no ...

Illustrates the use of monitors and options for viewing the solution computed by the eigensolvers, in various formats

Description of the different alternatives in SLEPc to compute eigenvalues located in different parts of the spectrum, including exterior, interior, inside an interval or inside a region

Illustrates how to solve eigenvalue problems where the matrix is defined in an implicit way, by means of a matrix-vector product subroutine (and possibly other operations)

How SLEPc can be used to solve standard eigenvalue problems. The different options are illustrated with an interactive example.