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A comprehensive collection of matrix data structures, linear solvers, least squares methods,
eigenvalue, and singular value decompositions.
/*
* Copyright (C) 2003-2006 Bjørn-Ove Heimsund
*
* This file is part of MTJ.
*
* This library is free software; you can redistribute it and/or modify it
* under the terms of the GNU Lesser General Public License as published by the
* Free Software Foundation; either version 2.1 of the License, or (at your
* option) any later version.
*
* This library is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
* FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License
* for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this library; if not, write to the Free Software Foundation,
* Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*/
package no.uib.cipr.matrix;
import java.util.Iterator;
import org.netlib.blas.BLAS;
import org.netlib.lapack.LAPACK;
import org.netlib.util.intW;
/**
* Partial implementation of a triangular, packed matrix
*/
abstract class AbstractTriangPackMatrix extends AbstractPackMatrix {
/**
* Upper or lower triangular
*/
UpLo uplo;
/**
* If the matrix is unit diagonal or not unit
*/
Diag diag;
/**
* Constructor for AbstractTriangPackMatrix
*/
AbstractTriangPackMatrix(int n, UpLo uplo, Diag diag) {
super(n);
this.uplo = uplo;
this.diag = diag;
}
/**
* Constructor for AbstractTriangPackMatrix
*/
AbstractTriangPackMatrix(Matrix A, UpLo uplo, Diag diag) {
this(A, false, uplo, diag);
}
/**
* Constructor for AbstractTriangPackMatrix
*/
AbstractTriangPackMatrix(Matrix A, boolean deep, UpLo uplo, Diag diag) {
super(A, deep);
this.uplo = uplo;
this.diag = diag;
}
@Override
public Vector mult(double alpha, Vector x, Vector y) {
if (!(y instanceof DenseVector))
return super.mult(alpha, x, y);
checkMultAdd(x, y);
double[] yd = ((DenseVector) y).getData();
// y = alpha*x
y.set(alpha, x);
// y = A*z
BLAS.getInstance().dtpmv(uplo.netlib(), Transpose.NoTranspose.netlib(), diag.netlib(),
numRows, data, yd, 1);
return y;
}
@Override
public Vector transMult(double alpha, Vector x, Vector y) {
if (!(y instanceof DenseVector))
return super.transMult(alpha, x, y);
checkTransMultAdd(x, y);
double[] yd = ((DenseVector) y).getData();
// y = alpha*x
y.set(alpha, x);
// y = A*z
BLAS.getInstance().dtpmv(uplo.netlib(), Transpose.Transpose.netlib(), diag.netlib(),
numRows, data, yd, 1);
return y;
}
@Override
public Matrix solve(Matrix B, Matrix X) {
return solve(B, X, Transpose.NoTranspose);
}
@Override
public Vector solve(Vector b, Vector x) {
DenseMatrix B = new DenseMatrix(b, false), X = new DenseMatrix(x, false);
solve(B, X);
return x;
}
@Override
public Matrix transSolve(Matrix B, Matrix X) {
return solve(B, X, Transpose.Transpose);
}
@Override
public Vector transSolve(Vector b, Vector x) {
DenseMatrix B = new DenseMatrix(b, false), X = new DenseMatrix(x, false);
transSolve(B, X);
return x;
}
Matrix solve(Matrix B, Matrix X, Transpose trans) {
if (!(X instanceof DenseMatrix))
throw new UnsupportedOperationException("X must be a DenseMatrix");
checkSolve(B, X);
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();
else if (info.val < 0)
throw new IllegalArgumentException();
return X;
}
@Override
public Iterator iterator() {
return new TriangPackMatrixIterator();
}
private class TriangPackMatrixIterator extends RefMatrixIterator {
@Override
public MatrixEntry next() {
entry.update(row, column);
if (uplo == UpLo.Lower)
if (row < numRows - 1)
row++;
else {
column++;
row = column;
}
else { // uplo == UpLo.Upper
if (row < column)
row++;
else {
column++;
row = 0;
}
}
return entry;
}
}
}