Package org.netlib.util

Examples of org.netlib.util.intW

218
219
220
221
222
223
224
225
226
227
228
        checkSolve(B, X);

        double[] Xd = ((DenseMatrix) X).getData();

        X.set(B);
        intW info = new intW(0);
        LAPACK.getInstance().dgtsv(numRows, X.numColumns(),
                offDiag.clone(), diag.clone(), offDiag.clone(), Xd, Matrices.ld(numRows), info);

        if (info.val > 0)
            throw new MatrixSingularException();
View Full Code Here
240
241
242
243
244
245
246
247
248
249
250
        double[] Xd = ((DenseMatrix) X).getData();

        X.set(B);

        intW info = new intW(0);
        LAPACK.getInstance().dtrtrs(uplo.netlib(), trans.netlib(), diag.netlib(), numRows,
                X.numColumns(), data, Math.max(1, ld), Xd, Matrices.ld(numRows), info);

        if (info.val > 0)
            throw new MatrixSingularException();
View Full Code Here
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
        int lwork;

        // Query optimal workspace. First for computing the factorization
        {
            work = new double[1];
            intW info = new intW(0);
            LAPACK.getInstance().dgelqf(m, n, new double[0], Matrices.ld(m), new double[0],
                    work, -1, info);

            if (info.val != 0)
                lwork = m;
            else
                lwork = (int) work[0];
            lwork = Math.max(1, lwork);
            work = new double[lwork];
        }

        // Workspace needed for generating an explicit orthogonal matrix
        {
            workGen = new double[1];
            intW info = new intW(0);
            LAPACK.getInstance().dorglq(m, n, m, new double[0],
              Matrices.ld(m), new double[0], workGen, -1, info);

            if (info.val != 0)
                lwork = m;
View Full Code Here
100
101
102
103
104
105
106
107
108
109
110
            throw new IllegalArgumentException("L == null");

        /*
         * Calculate factorisation, and extract the triangular factor
         */
        intW info = new intW(0);
        LAPACK.getInstance().dgelqf(m, n, A.getData(), Matrices.ld(m), tau, work,
                work.length, info);

        if (info.val < 0)
            throw new IllegalArgumentException();
View Full Code Here
137
138
139
140
141
142
143
144
145
146
147
        double[] Xd = ((DenseMatrix) X).getData();

        X.set(B);

        intW info = new intW(0);
        LAPACK.getInstance().dtptrs(uplo.netlib(), trans.netlib(), diag.netlib(), numRows,
                X.numColumns(), data, Xd, Matrices.ld(numRows), info);

        if (info.val > 0)
            throw new MatrixSingularException();
View Full Code Here
119
120
121
122
123
124
125
126
127
128
129
        X.set(B);

        int[] ipiv = new int[numRows];

        intW info = new intW(0);
        LAPACK.getInstance().dspsv(uplo.netlib(), numRows, X.numColumns(),
                data.clone(), ipiv, Xd, Matrices.ld(numRows), info);

        if (info.val > 0)
            throw new MatrixSingularException();
View Full Code Here
158
159
160
161
162
163
164
165
166
167
168
        double[] Xd = ((DenseMatrix) X).getData();

        X.set(B);

        intW info = new intW(0);
        LAPACK.getInstance().dppsv(uplo.netlib(), numRows, X.numColumns(),
                data.clone(), Xd, Matrices.ld(numRows), info);

        if (info.val > 0)
            throw new MatrixNotSPDException();
View Full Code Here
106
107
108
109
110
111
112
113
114
115
116
        // Allocate factorization matrix. The factorization matrix will be
        // large enough to accomodate any pivots
        BandMatrix Af = new BandMatrix(this, kd, kd + kd);
        int[] ipiv = new int[numRows];

        intW info = new intW(0);
        LAPACK.getInstance().dgbsv(numRows, kd, kd, X.numColumns(),
                Af.getData(), Matrices.ld(2 * kd + kd + 1), ipiv, Xd,
                Matrices.ld(numRows), info);

        if (info.val > 0)
View Full Code Here
146
147
148
149
150
151
152
153
154
155
156
        double[] Xd = ((DenseMatrix) X).getData();

        X.set(B);

        intW info = new intW(0);
        LAPACK.getInstance().dpbsv(uplo.netlib(), numRows, kd, X.numColumns(),
                data.clone(), Matrices.ld(kd + 1), Xd, Matrices.ld(numRows), info);

        if (info.val > 0)
            throw new MatrixNotSPDException();
View Full Code Here
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
        int lwork;

        // Query optimal workspace. First for computing the factorization
        {
            work = new double[1];
            intW info = new intW(0);
            LAPACK.getInstance().dgeqlf(m, n, new double[0], Matrices.ld(m), new double[0],
                    work, -1, info);

            if (info.val != 0)
                lwork = n;
            else
                lwork = (int) work[0];
            lwork = Math.max(1, lwork);
            work = new double[lwork];
        }

        // Workspace needed for generating an explicit orthogonal matrix
        {
            workGen = new double[1];
            intW info = new intW(0);
            LAPACK.getInstance().dorgql(m, n, k, new double[0],
               Matrices.ld(m), new double[0], workGen, -1, info);

            if (info.val != 0)
                lwork = n;
View Full Code Here
TOP

Related Classes of org.netlib.util.intW

  • ch.akuhn.matrix.eigenvalues.AllEigenvalues
  • ch.akuhn.matrix.eigenvalues.FewEigenvalues
  • com.github.fommil.netlib.Dgetri
  • com.github.fommil.netlib.Dsaupd
  • com.nr.fft.FFT
  • com.nr.ran.HashAll
  • com.nr.sf.Bessik
  • com.nr.sf.Bessjy
  • com.nr.test.test_chapter1.Test_caldat
  • com.nr.test.test_chapter10.Test_Amebsa
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.