Example usage for org.apache.commons.math3.linear CholeskyDecomposition getL

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear CholeskyDecomposition getL.

Prototype

public RealMatrix getL() 

Source Link

Document

Returns the matrix L of the decomposition.

Usage

From source file:com.joptimizer.algebra.CholeskyDecompositionTest.java

/**
 * good decomposition./* w  w  w. j  av  a2  s . c  o m*/
 */
public void testDecomposition1() throws Exception {
    log.debug("testDecomposition1");
    RealMatrix P1 = new Array2DRowRealMatrix(new double[][] { { 8.08073550734687, 1.59028724315583 },
            { 1.59028724315583, 0.3250861184011492 } });
    CholeskyDecomposition cFact1 = new CholeskyDecomposition(P1);
    log.debug("L: " + cFact1.getL());
    log.debug("LT: " + cFact1.getLT());
    // check L.LT-Q=0
    RealMatrix P1Inv = cFact1.getL().multiply(cFact1.getLT());
    double norm1 = P1Inv.subtract(P1).getNorm();
    log.debug("norm1: " + norm1);
    assertTrue(norm1 < 1.E-12);
}

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From source file:com.joptimizer.algebra.CholeskyDecompositionTest.java

/**
 * poor decomposition./*from   w  ww.ja v  a 2s  . com*/
 * rescaling can help in doing it better
 */
public void testDecomposition2() throws Exception {
    log.debug("testDecomposition2");
    RealMatrix P1 = new Array2DRowRealMatrix(new double[][] { { 8.185301256666552E9, 1.5977225251367908E9 },
            { 1.5977225251367908E9, 3.118660129093004E8 } });
    CholeskyDecomposition cFact1 = new CholeskyDecomposition(P1);
    log.debug("L: " + cFact1.getL());
    log.debug("LT: " + cFact1.getLT());
    // check L.LT-Q=0
    double norm1 = cFact1.getL().multiply(cFact1.getLT()).subtract(P1).getNorm();
    log.debug("norm1: " + norm1);
    assertTrue(norm1 < 1.E-5);

    //poor precision, try to make it better

    //geometric eigenvalues mean
    DescriptiveStatistics ds = new DescriptiveStatistics(new double[] { 8.5E9, 0.00572 });
    RealMatrix P2 = P1.scalarMultiply(1. / ds.getGeometricMean());
    CholeskyDecomposition cFact2 = new CholeskyDecomposition(P2);
    log.debug("L: " + cFact2.getL());
    log.debug("LT: " + cFact2.getLT());
    // check L.LT-Q=0
    double norm2 = cFact2.getL().multiply(cFact2.getLT()).subtract(P2).getNorm();
    log.debug("norm2: " + norm2);
    assertTrue(norm2 < Utils.getDoubleMachineEpsilon());
}

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From source file:com.joptimizer.solvers.CholeskyTest.java

/**
 * poor decomposition./*from   ww  w . j av a2 s.c  o m*/
 * rescaling can help in doing it better
 */
public void testDecomposition2() throws Exception {
    log.debug("testDecomposition2");
    RealMatrix P1 = new Array2DRowRealMatrix(new double[][] { { 8.185301256666552E9, 1.5977225251367908E9 },
            { 1.5977225251367908E9, 3.118660129093004E8 } });
    CholeskyDecomposition cFact1 = new CholeskyDecomposition(P1);
    log.debug("L: " + cFact1.getL());
    log.debug("LT: " + cFact1.getLT());
    // check L.LT-Q=0
    double norm1 = cFact1.getL().multiply(cFact1.getLT()).subtract(P1).getNorm();
    log.debug("norm1: " + norm1);
    assertTrue(norm1 < 1.E-5);

    //poor precision, try to make it better

    //geometric eigenvalues mean
    DescriptiveStatistics ds = new DescriptiveStatistics(new double[] { 8.5E9, 0.00572 });
    RealMatrix P2 = P1.scalarMultiply(1. / ds.getGeometricMean());
    CholeskyDecomposition cFact2 = new CholeskyDecomposition(P2);
    log.debug("L: " + cFact2.getL());
    log.debug("LT: " + cFact2.getLT());
    // check L.LT-Q=0
    double norm2 = cFact2.getL().multiply(cFact2.getLT()).subtract(P2).getNorm();
    log.debug("norm2: " + norm2);
    assertTrue(norm2 < 1.E-9);
}

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From source file:com.opengamma.strata.math.impl.linearalgebra.CholeskyDecompositionCommonsResult.java

/**
 * Constructor./*from  ww  w .  j  av a 2s  .com*/
 * @param ch The result of the Cholesky decomposition.
 */
public CholeskyDecompositionCommonsResult(CholeskyDecomposition ch) {
    ArgChecker.notNull(ch, "Cholesky decomposition");
    _determinant = ch.getDeterminant();
    _l = CommonsMathWrapper.unwrap(ch.getL());
    _lt = CommonsMathWrapper.unwrap(ch.getLT());
    _solver = ch.getSolver();
}

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From source file:org.apache.sysml.runtime.matrix.data.LibCommonsMath.java

/**
 * Function to compute Cholesky decomposition of the given input matrix. 
 * The input must be a real symmetric positive-definite matrix.
 * /*from   w  w w  .  ja  v  a  2 s  .  c  om*/
 * @param in commons-math3 Array2DRowRealMatrix
 * @return matrix block
 * @throws DMLRuntimeException if DMLRuntimeException occurs
 */
private static MatrixBlock computeCholesky(Array2DRowRealMatrix in) throws DMLRuntimeException {
    if (!in.isSquare())
        throw new DMLRuntimeException("Input to cholesky() must be square matrix -- given: a "
                + in.getRowDimension() + "x" + in.getColumnDimension() + " matrix.");

    CholeskyDecomposition cholesky = new CholeskyDecomposition(in);
    RealMatrix rmL = cholesky.getL();

    return DataConverter.convertToMatrixBlock(rmL.getData());
}

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From source file:org.eclipse.dataset.LinearAlgebra.java

/**
 * Calculate Cholesky decomposition A = L L^T
 * @param a// ww  w .  ja  v a  2 s . co m
 * @return L
 */
public static Dataset calcCholeskyDecomposition(Dataset a) {
    CholeskyDecomposition cd = new CholeskyDecomposition(createRealMatrix(a));
    return createDataset(cd.getL());
}

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From source file:org.meteoinfo.math.linalg.LinalgUtil.java

/**
 * Calculates the Cholesky decomposition of a matrix.
 * The Cholesky decomposition of a real symmetric positive-definite matrix A consists of a 
 * lower triangular matrix L with same size such that: A = LLT. In a sense, this is 
 * the square root of A./*w w  w.ja v  a  2  s. co  m*/
 * @param a The given matrix.
 * @return Result array.
 */
public static Array cholesky(Array a) {
    Array r = Array.factory(DataType.DOUBLE, a.getShape());
    double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
    RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
    CholeskyDecomposition decomposition = new CholeskyDecomposition(matrix);
    RealMatrix L = decomposition.getL();
    int n = L.getColumnDimension();
    int m = L.getRowDimension();
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < n; j++) {
            r.setDouble(i * n + j, L.getEntry(i, j));
        }
    }

    return r;
}

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From source file:org.ujmp.commonsmath.AbstractCommonsMathDenseDoubleMatrix2D.java

public Matrix chol() {
    try {// ww w  .j av a  2  s . c  o m
        CholeskyDecomposition chol = new CholeskyDecomposition(matrix);
        Matrix l = CommonsMathDenseDoubleMatrix2DFactory.INSTANCE.dense(chol.getL());
        return l;
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
}

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