Examples of TridiagonalDecompositionHouseholder

mikera.matrixx.decompose.impl.hessenberg.TridiagonalDecompositionHouseholder

Performs a TridiagonalSimilarDecomposition similar tridiagonal decomposition on a square symmetric input matrix. Householder vectors perform the similar operation and the symmetry is taken advantage of for good performance.

Finds the decomposition of a matrix in the form of:

A = O*T*O^T

where A is a symmetric m by m matrix, O is an orthogonal matrix, and T is a tridiagonal matrix.

This implementation is based off of the algorithm described in:

David S. Watkins, "Fundamentals of Matrix Computations," Second Edition. Page 349-355
@author Peter Abeles
org.ejml.alg.dense.decomposition.hessenberg.TridiagonalDecompositionHouseholder

Performs a {@link TridiagonalSimilarDecomposition similar tridiagonal decomposition} on a square symmetric input matrix.Householder vectors perform the similar operation and the symmetry is taken advantage of for good performance.

Finds the decomposition of a matrix in the form of:

A = O*T*O^T

where A is a symmetric m by m matrix, O is an orthogonal matrix, and T is a tridiagonal matrix.

This implementation is based off of the algorithm described in:

David S. Watkins, "Fundamentals of Matrix Computations," Second Edition. Page 349-355
@author Peter Abeles

Examples of mikera.matrixx.decompose.impl.hessenberg.TridiagonalDecompositionHouseholder

                {-0.098491, 2.776741, 0.623341, 0.624798, 0.401906},
                {-0.397037, 0.623341, 3.571302, -0.239631, -0.264573},
                {0.367426, 0.624798, -0.239631, 3.625034, -0.162896},
                {-0.208338, 0.401906, -0.264573, -0.162896, 3.835783}});


        TridiagonalDecompositionHouseholder tridiag = new TridiagonalDecompositionHouseholder();
        tridiag.decompose(A);


        double diag[] = new double[5];
        double off[] = new double[4];


        tridiag.getDiagonal(diag,off);


        SymmetricQrAlgorithm alg = new SymmetricQrAlgorithm();


        assertTrue(alg.process(5,diag,off));

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Examples of mikera.matrixx.decompose.impl.hessenberg.TridiagonalDecompositionHouseholder

        vector = new SymmetricQrAlgorithm(helper);
    }


    public SymmetricQRAlgorithmDecomposition( boolean computeVectors ) {


        this(new TridiagonalDecompositionHouseholder(), computeVectors);
    }

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Examples of org.ejml.alg.dense.decomposition.hessenberg.TridiagonalDecompositionHouseholder

     */
    public static TridiagonalSimilarDecomposition<DenseMatrix64F> tridiagonal(  int matrixSize ) {
        if( matrixSize >= 1800 ) {
            return new TridiagonalDecompositionBlock();
        } else {
            return new TridiagonalDecompositionHouseholder();
        }
    }

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Examples of org.ejml.alg.dense.decomposition.hessenberg.TridiagonalDecompositionHouseholder

     */
    @Test
    public void multipleEigenvalues() {
        DenseMatrix64F A = new DenseMatrix64F(5,5, true, 2.191140, -0.098491, -0.397037, 0.367426, -0.208338, -0.098491, 2.776741, 0.623341, 0.624798, 0.401906, -0.397037, 0.623341, 3.571302, -0.239631, -0.264573, 0.367426, 0.624798, -0.239631, 3.625034, -0.162896, -0.208338, 0.401906, -0.264573, -0.162896, 3.835783);


        TridiagonalDecompositionHouseholder tridiag = new TridiagonalDecompositionHouseholder();
        tridiag.decompose(A);


        double diag[] = new double[5];
        double off[] = new double[4];


        tridiag.getDiagonal(diag,off);


        SymmetricQrAlgorithm alg = new SymmetricQrAlgorithm();


        assertTrue(alg.process(5,diag,off));

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Examples of org.ejml.alg.dense.decomposition.hessenberg.TridiagonalDecompositionHouseholder


        return System.currentTimeMillis() - prev;
    }


    public static long standardTridiag( DenseMatrix64F orig , int numTrials ) {
        TridiagonalSimilarDecomposition<DenseMatrix64F> decomp = new TridiagonalDecompositionHouseholder();
        SymmetricQRAlgorithmDecomposition alg = new SymmetricQRAlgorithmDecomposition(decomp,true);


        long prev = System.currentTimeMillis();


        for( long i = 0; i < numTrials; i++ ) {

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