Examples of org.ejml.alg.dense.decomposition.chol.CholeskyDecompositionLDL

Package org.ejml.alg.dense.decomposition.chol

Examples of org.ejml.alg.dense.decomposition.chol.CholeskyDecompositionLDL

org.ejml.alg.dense.decomposition.chol.CholeskyDecompositionLDL

This variant on the Cholesky decomposition avoid the need to take the square root by performing the following decomposition:

L*D*L^T=A

where L is a lower triangular matrix with zeros on the diagonal. D is a diagonal matrix. The diagonal elements of L are equal to one.

Unfortunately the speed advantage of not computing the square root is washed out by the increased number of array accesses. There only appears to be a slight speed boost for very small matrices.
@author Peter Abeles

     *
     * @param matrixSize Number of rows and columns that the returned decomposition is optimized for.
     * @return CholeskyDecompositionLDL
     */
    public static CholeskyDecompositionLDL cholLDL( int matrixSize ) {
        return new CholeskyDecompositionLDL();
    }

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    public LinearSolverCholLDL( CholeskyDecompositionLDL decomp ) {
        this.decomp = decomp;
    }


    public LinearSolverCholLDL() {
        this.decomp = new CholeskyDecompositionLDL();
    }

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    {
        System.out.println("Solve Cholesky         = "+solve(
                new LinearSolverChol(new CholeskyDecompositionInner(true)),
                A,b,numTrials));
        System.out.println("Solve Cholesky LDL     = "+solve(
                new LinearSolverCholLDL(new CholeskyDecompositionLDL()),
                A,b,numTrials));
    }

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Related Classes of org.ejml.alg.dense.decomposition.chol.CholeskyDecompositionLDL

org.ejml.alg.dense.linsol.BenchmarkSolveSymPosDef

org.ejml.alg.dense.linsol.chol.LinearSolverCholLDL

org.ejml.data.DenseMatrix64F

org.ejml.factory.DecompositionFactory

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