slepc-3.10.2 2019-02-11
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Eigenvalue Problem Solver - EPS: Examples

The Eigenvalue Problem Solver (EPS) is the object provided by SLEPc for specifying a linear eigenvalue problem, either in standard or generalized form. It provides uniform and efficient access to all of the linear eigensolvers included in the package.

EPS users can set various options at runtime via the options database (e.g., -eps_nev 4 -eps_type arnoldi). Options can also be set directly in application codes by calling the corresponding routines (e.g., EPSSetDimensions() / EPSSetType()).

ex1.c: Standard symmetric eigenproblem corresponding to the Laplacian operator in 1 dimension
ex2.c: Standard symmetric eigenproblem corresponding to the Laplacian operator in 2 dimensions
ex3.c: Solves the same eigenproblem as in example ex2, but using a shell matrix
ex4.c: Solves a standard eigensystem Ax=kx with the matrix loaded from a file
ex5.c: Eigenvalue problem associated with a Markov model of a random walk on a triangular grid
ex7.c: Solves a generalized eigensystem Ax=kBx with matrices loaded from a file
ex9.c: Solves a problem associated to the Brusselator wave model in chemical reactions, illustrating the use of shell matrices
ex10.c: Illustrates the use of shell spectral transformations
ex11.c: Computes the smallest nonzero eigenvalue of the Laplacian of a graph
ex12.c: Compute all eigenvalues in an interval of a symmetric-definite problem
ex13.c: Generalized Symmetric eigenproblem
ex18.c: Solves the same problem as in ex5, but with a user-defined sorting criterion
ex19.c: Standard symmetric eigenproblem for the 3-D Laplacian built with the DM interface
ex24.c: Spectrum folding for a standard symmetric eigenproblem
ex25.c: Spectrum slicing on generalized symmetric eigenproblem
ex29.c: Solves the same problem as in ex5, with a user-defined stopping test
ex30.c: Illustrates the use of a region for filtering; the number of wanted eigenvalues in not known a priori
ex31.c: Power grid small signal stability analysis of WECC 9 bus system
ex34.c: Nonlinear inverse iteration for A(x)*x=lambda*B(x)*x
ex35.c: Shell spectral transformations with a non-injective mapping
ex36.c: Use the matrix exponential to compute rightmost eigenvalues