Example usage for org.apache.commons.math.linear LUDecomposition getSolver

Introduction

In this page you can find the example usage for org.apache.commons.math.linear LUDecomposition getSolver.

Prototype

DecompositionSolver getSolver();

Source Link

Document

Get a solver for finding the A × X = B solution in exact linear sense.

Usage

From source file:adams.data.utils.SavitzkyGolay.java

/**
 * Determines the coefficients for the smoothing, with optional debugging
 * output.//from  ww  w .  ja  v a  2s. c  o m
 *
 * @param numLeft   the number of points to the left
 * @param numRight   the number of points to the right
 * @param polyOrder   the polynomial order
 * @param derOrder   the derivative order
 * @param debug   whether to output debugging information
 * @return      the coefficients
 */
public static double[] determineCoefficients(int numLeft, int numRight, int polyOrder, int derOrder,
        boolean debug) {
    double[] result;
    RealMatrix A;
    int i;
    int j;
    int k;
    float sum;
    RealMatrix b;
    LUDecomposition lu;
    RealMatrix solution;

    result = new double[numLeft + numRight + 1];

    // no window?
    if (result.length == 1) {
        result[0] = 1.0;
        return result;
    }

    // Note: "^" = superscript, "." = subscript

    // {A^T*A}.ij = Sum[k:-nl..nr](k^(i+j))
    A = new Array2DRowRealMatrix(polyOrder + 1, polyOrder + 1);
    for (i = 0; i < A.getRowDimension(); i++) {
        for (j = 0; j < A.getColumnDimension(); j++) {
            sum = 0;
            for (k = -numLeft; k <= numRight; k++)
                sum += Math.pow(k, i + j);
            A.setEntry(i, j, sum);
        }
    }
    if (debug)
        System.out.println("A:\n" + A);

    // LU decomp for inverse matrix
    b = new Array2DRowRealMatrix(polyOrder + 1, 1);
    b.setEntry(derOrder, 0, 1.0);
    if (debug)
        System.out.println("b:\n" + b);

    lu = new LUDecompositionImpl(A);
    solution = lu.getSolver().solve(b);
    if (debug)
        System.out.println("LU decomp. - solution:\n" + solution);

    // coefficients: c.n = Sum[m:0..M]((A^T*A)^-1).0m * n^m with n=-nl..nr
    for (i = -numLeft; i <= numRight; i++) {
        sum = 0;
        for (j = 0; j <= polyOrder; j++)
            sum += solution.getEntry(j, 0) * Math.pow(i, j);
        result[i + numLeft] = sum;
    }
    if (debug)
        System.out.println("Coefficients:\n" + Utils.arrayToString(result));

    return result;
}

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From source file:com.opengamma.analytics.math.linearalgebra.LUDecompositionCommonsResult.java

/**
 * @param lu The result of the LU decomposition, not null. $\mathbf{L}$ cannot be singular.
 *//*from w  w  w. j  a v a  2s. c o  m*/
public LUDecompositionCommonsResult(final LUDecomposition lu) {
    Validate.notNull(lu, "LU decomposition");
    Validate.notNull(lu.getL(), "Matrix is singular; could not perform LU decomposition");
    _determinant = lu.getDeterminant();
    _l = CommonsMathWrapper.unwrap(lu.getL());
    _p = CommonsMathWrapper.unwrap(lu.getP());
    _pivot = lu.getPivot();
    _solver = lu.getSolver();
    _u = CommonsMathWrapper.unwrap(lu.getU());
}

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From source file:org.interpss.core.sparse.UCTE2000SparseMatrixCasesTest.java

@Test
public void testCase1() throws Exception {
    AclfNetwork net = CorePluginObjFactory.getFileAdapter(IpssFileAdapter.FileFormat.IEEECDF)
            .load("testData/ieee_format/UCTE_2000_WinterOffPeak.ieee").getAclfNet();

    SparseEqnMatrix2x2 eqn = net.formJMatrix();

    RealMatrix m = sparseMatrix2Ary(eqn);

    double[] b = new double[m.getRowDimension()];
    for (int i = 0; i < m.getRowDimension(); i++)
        b[i] = 1.0;/*from w w  w.  ja v a  2s.co  m*/

    System.out.println("Common Math ... ");
    long starttime = System.currentTimeMillis();
    LUDecomposition lu = new LUDecompositionImpl(m);
    double[] result = lu.getSolver().solve(b);
    System.out.println("time for full matrix : " + (System.currentTimeMillis() - starttime) * 0.001);

    //      int cnt = 0;
    //      for (double d : result)
    //         System.out.println(cnt++ + ", " + d);

    int n = eqn.getDimension() / 2;
    for (int i = 0; i < n; i++)
        eqn.setB(new Vector_xy(1.0, 1.0), i + 1);

    System.out.println("Interpss ... ");
    starttime = System.currentTimeMillis();
    eqn.luMatrixAndSolveEqn(1.0e-20);
    System.out.println("time for spase matrix : " + (System.currentTimeMillis() - starttime) * 0.001);

    int cnt = 0;
    for (int i = 0; i < n; i++) {
        Vector_xy xy = eqn.getX(i + 1);
        //System.out.println(cnt++ + ", " + xy.x);
        //System.out.println(cnt++ + ", " + xy.y);
        assertTrue(Math.abs(result[cnt++] - xy.x) < 0.0001);
        assertTrue(Math.abs(result[cnt++] - xy.y) < 0.0001);
    }

    /*
    J-matrix Dimension:2508
    time for full matrix : 54.321
    time for sparse matrix : 0.197
     */
}

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From source file:org.smurn.jsift.LUSolver3.java

@Override
public double[] solve(double[][] m, double[] b) {
    if (m == null || b == null) {
        throw new NullPointerException();
    }//from w  w  w  .ja v a 2s  .  co m
    if (b.length != 3) {
        throw new IllegalArgumentException();
    }
    if (m.length != 3) {
        throw new IllegalArgumentException();
    }
    try {
        RealMatrix matrix = new Array2DRowRealMatrix(m);
        LUDecomposition lu = new LUDecompositionImpl(matrix);
        DecompositionSolver solver = lu.getSolver();
        if (!solver.isNonSingular()) {
            return null;
        }
        return solver.solve(b);
    } catch (NonSquareMatrixException e) {
        throw new IllegalArgumentException();
    }
}

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