ex5.py: Simple quadratic eigenvalue problem — slepc4py 3.24.2 documentation

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# ex5.py: Simple quadratic eigenvalue problem

This example solves a polynomial eigenvalue problem of degree 2, which is the most commonly found in applications. Larger degree polynomials are handled similarly. The coefficient matrices in this example do not come from an application, they are just simple matrices that are easy to build, just to illustrate how it works. The eigenvalues in this example are purely imaginary and come in conjugate pairs.

The full source code for this demo can be downloaded here.

Initialization is similar to previous examples.

    import sys, slepc4py
    slepc4py.init(sys.argv)

    from petsc4py import PETSc
    from slepc4py import SLEPc

    Print = PETSc.Sys.Print

A function to build the matrices. The lowest degree coefficient is the 2-D Laplacian, the highest degree one is the identity matrix, and the other matrix is set to zero (which means that this problem could have been solved as a linear eigenproblem).

    def construct_operators(m,n):
        Print("Quadratic Eigenproblem, N=%d (%dx%d grid)"% (m*n, m, n))
        # K is the 2-D Laplacian
        K = PETSc.Mat().create()
        K.setSizes([n*m, n*m])
        K.setFromOptions()
        Istart, Iend = K.getOwnershipRange()
        for I in range(Istart,Iend):
            v = -1.0; i = I//n; j = I-i*n;
            if i>0:
                J=I-n; K[I,J] = v
            if i<m-1:
                J=I+n; K[I,J] = v
            if j>0:
                J=I-1; K[I,J] = v
            if j<n-1:
                J=I+1; K[I,J] = v
            v=4.0; K[I,I] = v
        K.assemble()
        # C is the zero matrix
        C = PETSc.Mat().create()
        C.setSizes([n*m, n*m])
        C.setFromOptions()
        C.assemble()
        # M is the identity matrix
        M = PETSc.Mat().createConstantDiagonal([n*m, n*m], 1.0)

        return M, C, K

The polynomial eigenvalue solver is similar to the linear eigensolver used in previous examples. The main difference is that we must provide a list of matrices, from lowest to highest degree.

    def solve_eigensystem(M, C, K):
        # Setup the eigensolver
        Q = SLEPc.PEP().create()
        Q.setOperators([K, C, M])
        Q.setDimensions(6)
        Q.setProblemType(SLEPc.PEP.ProblemType.GENERAL)
        Q.setFromOptions()
        # Solve the eigensystem
        Q.solve()
        # Create the result vectors
        xr, xi = K.createVecs()

        its = Q.getIterationNumber()
        Print("Number of iterations of the method: %i" % its)
        sol_type = Q.getType()
        Print("Solution method: %s" % sol_type)
        nev, ncv, mpd = Q.getDimensions()
        Print("")
        Print("Number of requested eigenvalues: %i" % nev)
        tol, maxit = Q.getTolerances()
        Print("Stopping condition: tol=%.4g, maxit=%d" % (tol, maxit))
        nconv = Q.getConverged()
        Print("Number of converged approximate eigenpairs: %d" % nconv)
        if nconv > 0:
            Print("")
            Print("          k           ||(k^2M+Ck+K)x||/||kx|| ")
            Print("-------------------- -------------------------")
            for i in range(nconv):
                k = Q.getEigenpair(i, xr, xi)
                error = Q.computeError(i)
                if k.imag != 0.0:
                    Print("%9f%+9f j    %12g" % (k.real, k.imag, error))
                else:
                    Print("%12f         %12g" % (k.real, error))
        Print("")

The main program simply processes two user-defined command-line options (the dimensions of the mesh) and calls the other two functions.

    if __name__ == '__main__':
        opts = PETSc.Options()
        m = opts.getInt('m', 32)
        n = opts.getInt('n', m)
        M, C, K = construct_operators(m,n)
        solve_eigensystem(M, C, K)
        M = C = K = None