Package no.uib.cipr.matrix

Examples of no.uib.cipr.matrix.DenseMatrix

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    protected void setUp() throws Exception {
        int size = Utilities.getInt(1, 8);
        coll = new CollectiveCommunications(size);

        int n = Utilities.getInt(size, 250);
        A_unsymm = new DenseMatrix(n, n);
        A_symm = new UpperSymmDenseMatrix(n);

        Utilities.populate(A_unsymm);
        Utilities.upperPopulate(A_unsymm);
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        if (restart <= 0)
            throw new IllegalArgumentException(
                    "restart must be a positive integer");

        s = new DenseVector(restart + 1);
        H = new DenseMatrix(restart + 1, restart);
        rotation = new GivensRotation[restart + 1];

        v = new Vector[restart + 1];
        for (int i = 0; i < v.length; ++i)
            v[i] = r.copy().zero();
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        Il.toArray(I);
        for (int i = 0; i < Al.size() - 1; ++i)
            this.A[i] = Al.get(i);

        // Create a LU decomposition of the smallest Galerkin matrix
        DenseMatrix Ac = new DenseMatrix(Al.get(Al.size() - 1));
        lu = new DenseLU(Ac.numRows(), Ac.numColumns());
        lu.factor(Ac);

        // Allocate vectors at each level
        u = new DenseVector[m];
        f = new DenseVector[m];
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     * Solves directly at the coarsest level
     */
    private void directSolve() {
        int k = m - 1;
        u[k].set(f[k]);
        DenseMatrix U = new DenseMatrix(u[k], false);

        if (transpose)
            lu.transSolve(U);
        else
            lu.solve(U);
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        // Allocate space for the decomposition
        Wr = new double[n];
        Wi = new double[n];

        if (left)
            Vl = new DenseMatrix(n, n);
        else
            Vl = null;

        if (right)
            Vr = new DenseMatrix(n, n);
        else
            Vr = null;

        // Find the needed workspace
        double[] worksize = new double[1];
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     *            Matrix to factorize. Not modified
     * @return Newly allocated decomposition
     * @throws NotConvergedException
     */
    public static NativeEVD factorize(DenseMatrix A) throws NotConvergedException {
        return new NativeEVD(A.numRows()). factor(new DenseMatrix(A));
    }
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    Variable v1 = new Variable (Variable.CONTINUOUS);
    Variable v2 = new Variable (Variable.CONTINUOUS);
    Randoms r = new Randoms (2343);

    Vector mu = new DenseVector (new double[] { 1.0, 2.0 });
    Matrix var = new DenseMatrix (new double[][] {{ 0.5, 2.0 }, { 0, 1 }});
//    Matrix var = new DenseMatrix (new double[][] {{ 0.5, 2.0 }, { 2.0, 0.75 }});

    VarSet vars = new HashVarSet (new Variable[] { v1, v2 });
    Factor f = new NormalFactor (vars, mu, var);
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        assertEquals(A, QR.factorize(A));
    }

    public void testFactor() {
        QR qr = new QR(A.numRows(), A.numColumns());
        assertEquals(A, qr.factor(new DenseMatrix(A)));
    }
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        assertEquals(A, qr.factor(new DenseMatrix(A)));
    }

    public void testRepeatFactor() {
        QR qr = new QR(A.numRows(), A.numColumns());
        qr.factor(new DenseMatrix(A));
        assertEquals(A, qr);
        qr.factor(new DenseMatrix(A));
        assertEquals(A, qr);
    }
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        assertEquals(Ar, QR.factorize(Ar));
    }

    public void testFactorNonSquare() {
        QR qr = new QR(Ar.numRows(), Ar.numColumns());
        assertEquals(Ar, qr.factor(new DenseMatrix(Ar)));
    }
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Related Classes of no.uib.cipr.matrix.DenseMatrix

  • cc.mallet.grmm.test.TestNormalFactor
  • gov.sandia.dfgleic.TSQR$TSQRIteration
  • no.uib.cipr.matrix.BandCholeskyTest
  • no.uib.cipr.matrix.BandLUTest
  • no.uib.cipr.matrix.DenseCholeskyTest
  • no.uib.cipr.matrix.DenseLUTest
  • no.uib.cipr.matrix.DenseMatrixTest
  • no.uib.cipr.matrix.distributed.DistIterativeSolverTest
  • no.uib.cipr.matrix.distributed.DistIterativeSolverTest$ColumnDistIterativeSolver
  • no.uib.cipr.matrix.distributed.DistIterativeSolverTest$RowDistIterativeSolver
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