Documentation of 'jvx.numeric.PnLanczos' Java class
PnLanczos ("JavaView Reference Manual")
"JavaView? v5.03.003"
jvx.numeric

Class PnLanczos



  • public class PnLanczos
    extends java.lang.Object
    Lanczos method - computes (incomplete) tridiagonalization T of an symmetric input matrix A. Eigenvalues of T approximates extreme eigenvalues of A. A is only given through a vector-matrix-multiplication.
    Author:
    Christoph von Tycowicz
    Version:
    28.08.2008, 1.00 created (cvt)
    • Method Detail

      • tridiagonalize

        public void tridiagonalize(PdVector diagonal,
                                   PdVector offDiagonal,
                                   int dimension)
        Compute tridiagonalization T of symmetric matrix.
        Parameters:
        diagonal - vector holding diagonal elements of tridiagonalization
        offDiagonal - vector holding off-diagonal elements
        dimension - determines dimension of T
      • getRitzVector

        public PdVector getRitzVector(PdVector v)
        Compute Ritz-vector using lanczos vector.
        Parameters:
        v - eigenvector of tridiagonalization
        Returns:
        eigenvector of input matrix
      • computeEigenpairs

        public void computeEigenpairs(PdVector values,
                                      PdVector[] vectors,
                                      int count)
        Compute spectrum of incomplete tridiagonalization T.
        Parameters:
        values - elements will be overriden with eigenvalues
        vectors - empty array for storing the eigenvectors
        count - number of computed eigenpairs
      • computeEigenpairs

        public void computeEigenpairs(PdVector values,
                                      PdVector[] vectors,
                                      int count,
                                      int offset,
                                      int dim)
        Compute spectrum of incomplete tridiagonalization T.
        Parameters:
        values - elements will be overriden with eigenvalues
        vectors - empty array for storing the eigenvectors
        count - number of computed eigenpairs
        offset - startindex for the results in values and vectors
        dim - dimension of tridiagonalization (must be greater than count, at most dimension of eigenproblem)
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