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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 no.uib.cipr.matrix.Matrix.Norm;
import com.github.fommil.netlib.LAPACK;
import org.netlib.util.doubleW;
import org.netlib.util.intW;
/**
* Dense Cholesky decomposition
*/
public class DenseCholesky {
/**
* Matrix dimension
*/
private final int n;
/**
* Cholesky decomposition of a lower matrix
*/
private LowerTriangDenseMatrix Cl;
/**
* Cholesky decomposition of an upper matrix
*/
private UpperTriangDenseMatrix Cu;
/**
* If the matrix is SPD or not
*/
private boolean notspd;
/**
* True for upper part, else false
*/
private final boolean upper;
/**
* Constructor for DenseCholesky
*
* @param n
* Matrix size
* @param upper
* True for decomposing an upper symmetrical matrix, false for a
* lower symmetrical matrix
*/
public DenseCholesky(int n, boolean upper) {
this.n = n;
this.upper = upper;
if (upper)
Cu = new UpperTriangDenseMatrix(n);
else
Cl = new LowerTriangDenseMatrix(n);
}
/**
* Calculates a Cholesky decomposition
*
* @param A
* Matrix to decompose. Not modified
* @return The current decomposition
*/
public static DenseCholesky factorize(Matrix A) {
return new DenseCholesky(A.numRows(), true)
.factor(new UpperSPDDenseMatrix(A));
}
/**
* Calculates a Cholesky decomposition
*
* @param A
* Matrix to decompose. Overwritten on return
* @return The current decomposition
*/
public DenseCholesky factor(LowerSPDDenseMatrix A) {
if (upper)
throw new IllegalArgumentException(
"Cholesky decomposition constructed for upper matrices");
return decompose(A);
}
/**
* Calculates a Cholesky decomposition
*
* @param A
* Matrix to decompose. Overwritten on return
* @return The current decomposition
*/
public DenseCholesky factor(UpperSPDDenseMatrix A) {
if (!upper)
throw new IllegalArgumentException(
"Cholesky decomposition constructed for lower matrices");
return decompose(A);
}
private DenseCholesky decompose(AbstractDenseMatrix A) {
if (n != A.numRows())
throw new IllegalArgumentException("n != A.numRows()");
notspd = false;
intW info = new intW(0);
if (upper)
LAPACK.getInstance().dpotrf(UpLo.Upper.netlib(), A.numRows(),
A.getData(), Matrices.ld(A.numRows()), info);
else
LAPACK.getInstance().dpotrf(UpLo.Lower.netlib(), A.numRows(),
A.getData(), Matrices.ld(A.numRows()), info);
if (info.val > 0)
notspd = true;
else if (info.val < 0)
throw new IllegalArgumentException();
if (upper)
Cu.set(A);
else
Cl.set(A);
return this;
}
/**
* Returns true if the matrix decomposed is symmetrical, positive definite
*/
public boolean isSPD() {
return !notspd;
}
/**
* Returns the decomposition matrix. Only valid for decomposition of a lower
* SPD matrix
*/
public LowerTriangDenseMatrix getL() {
if (!upper)
return Cl;
else
throw new UnsupportedOperationException();
}
/**
* Returns the decomposition matrix. Only valid for decomposition of a upper
* SPD matrix
*/
public UpperTriangDenseMatrix getU() {
if (upper)
return Cu;
else
throw new UnsupportedOperationException();
}
/**
* Solves for B, overwriting it on return
*/
public DenseMatrix solve(DenseMatrix B) throws MatrixNotSPDException {
if (notspd)
throw new MatrixNotSPDException();
if (n != B.numRows())
throw new IllegalArgumentException("n != B.numRows()");
intW info = new intW(0);
if (upper)
LAPACK.getInstance().dpotrs(UpLo.Upper.netlib(), Cu.numRows(),
B.numColumns(), Cu.getData(), Matrices.ld(Cu.numRows()),
B.getData(), Matrices.ld(Cu.numRows()), info);
else
LAPACK.getInstance().dpotrs(UpLo.Lower.netlib(), Cl.numRows(),
B.numColumns(), Cl.getData(), Matrices.ld(Cl.numRows()),
B.getData(), Matrices.ld(Cl.numRows()), info);
if (info.val < 0)
throw new IllegalArgumentException();
return B;
}
/**
* Computes the reciprocal condition number
*
* @param A
* The matrix this is a decomposition of
* @return The reciprocal condition number. Values close to unity indicate a
* well-conditioned system, while numbers close to zero do not.
*/
public double rcond(Matrix A) {
if (n != A.numRows())
throw new IllegalArgumentException("n != A.numRows()");
if (!A.isSquare())
throw new IllegalArgumentException("!A.isSquare()");
double anorm = A.norm(Norm.One);
double[] work = new double[3 * n];
int[] iwork = new int[n];
intW info = new intW(0);
doubleW rcond = new doubleW(0);
if (upper)
LAPACK.getInstance().dpocon(UpLo.Upper.netlib(), n, Cu.getData(),
Matrices.ld(n), anorm, rcond, work, iwork, info);
else
LAPACK.getInstance().dpocon(UpLo.Lower.netlib(), n, Cl.getData(),
Matrices.ld(n), anorm, rcond, work, iwork, info);
if (info.val < 0)
throw new IllegalArgumentException();
return rcond.val;
}
}