source: branches/3.2/sources/HeuristicLab.ExtLibs/HeuristicLab.ALGLIB/2.1.2.2591/ALGLIB-2.1.2.2591/sevd.cs @ 4539

/*************************************************************************
Copyright (c) 2005-2007, Sergey Bochkanov (ALGLIB project).

>>> SOURCE LICENSE >>>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation (www.fsf.org); either version 2 of the 
License, or (at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

A copy of the GNU General Public License is available at
http://www.fsf.org/licensing/licenses

>>> END OF LICENSE >>>
*************************************************************************/

using System;

namespace alglib
{
    public class sevd
    {
        /*************************************************************************
        Finding the eigenvalues and eigenvectors of a symmetric matrix

        The algorithm finds eigen pairs of a symmetric matrix by reducing it to
        tridiagonal form and using the QL/QR algorithm.

        Input parameters:
            A       -   symmetric matrix which is given by its upper or lower
                        triangular part.
                        Array whose indexes range within [0..N-1, 0..N-1].
            N       -   size of matrix A.
            IsUpper -   storage format.
            ZNeeded -   flag controlling whether the eigenvectors are needed or not.
                        If ZNeeded is equal to:
                         * 0, the eigenvectors are not returned;
                         * 1, the eigenvectors are returned.

        Output parameters:
            D       -   eigenvalues in ascending order.
                        Array whose index ranges within [0..N-1].
            Z       -   if ZNeeded is equal to:
                         * 0, Z hasn’t changed;
                         * 1, Z contains the eigenvectors.
                        Array whose indexes range within [0..N-1, 0..N-1].
                        The eigenvectors are stored in the matrix columns.

        Result:
            True, if the algorithm has converged.
            False, if the algorithm hasn't converged (rare case).

          -- ALGLIB --
             Copyright 2005-2008 by Bochkanov Sergey
        *************************************************************************/
        public static bool smatrixevd(double[,] a,
            int n,
            int zneeded,
            bool isupper,
            ref double[] d,
            ref double[,] z)
        {
            bool result = new bool();
            double[] tau = new double[0];
            double[] e = new double[0];

            a = (double[,])a.Clone();

            System.Diagnostics.Debug.Assert(zneeded==0 | zneeded==1, "SMatrixEVD: incorrect ZNeeded");
            tridiagonal.smatrixtd(ref a, n, isupper, ref tau, ref d, ref e);
            if( zneeded==1 )
            {
                tridiagonal.smatrixtdunpackq(ref a, n, isupper, ref tau, ref z);
            }
            result = tdevd.smatrixtdevd(ref d, e, n, zneeded, ref z);
            return result;
        }


        public static bool symmetricevd(double[,] a,
            int n,
            int zneeded,
            bool isupper,
            ref double[] d,
            ref double[,] z)
        {
            bool result = new bool();
            double[] tau = new double[0];
            double[] e = new double[0];

            a = (double[,])a.Clone();

            System.Diagnostics.Debug.Assert(zneeded==0 | zneeded==1, "SymmetricEVD: incorrect ZNeeded");
            tridiagonal.totridiagonal(ref a, n, isupper, ref tau, ref d, ref e);
            if( zneeded==1 )
            {
                tridiagonal.unpackqfromtridiagonal(ref a, n, isupper, ref tau, ref z);
            }
            result = tdevd.tridiagonalevd(ref d, e, n, zneeded, ref z);
            return result;
        }
    }
}