org.ejml.dense.row.linsol.qr.LinearSolverQr_DDRM Maven / Gradle / Ivy
/*
* Copyright (c) 2009-2018, Peter Abeles. All Rights Reserved.
*
* This file is part of Efficient Java Matrix Library (EJML).
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.ejml.dense.row.linsol.qr;
import org.ejml.data.DMatrixRMaj;
import org.ejml.dense.row.CommonOps_DDRM;
import org.ejml.dense.row.SpecializedOps_DDRM;
import org.ejml.dense.row.decomposition.TriangularSolver_DDRM;
import org.ejml.dense.row.linsol.LinearSolverAbstract_DDRM;
import org.ejml.interfaces.decomposition.QRDecomposition;
/**
*
* A solver for a generic QR decomposition algorithm. This will in general be a bit slower than the
* specialized once since the full Q and R matrices need to be extracted.
*
*
* It solve for x by first multiplying b by the transpose of Q then solving for the result.
*
* QRx=b
* Rx=Q^T b
*
*
* @author Peter Abeles
*/
public class LinearSolverQr_DDRM extends LinearSolverAbstract_DDRM {
private QRDecomposition decomposer;
protected int maxRows = -1;
protected int maxCols = -1;
protected DMatrixRMaj Q;
protected DMatrixRMaj R;
private DMatrixRMaj Y,Z;
/**
* Creates a linear solver that uses QR decomposition.
*
*/
public LinearSolverQr_DDRM(QRDecomposition decomposer) {
this.decomposer = decomposer;
}
/**
* Changes the size of the matrix it can solve for
*
* @param maxRows Maximum number of rows in the matrix it will decompose.
* @param maxCols Maximum number of columns in the matrix it will decompose.
*/
public void setMaxSize( int maxRows , int maxCols )
{
this.maxRows = maxRows; this.maxCols = maxCols;
Q = new DMatrixRMaj(maxRows,maxRows);
R = new DMatrixRMaj(maxRows,maxCols);
Y = new DMatrixRMaj(maxRows,1);
Z = new DMatrixRMaj(maxRows,1);
}
/**
* Performs QR decomposition on A
*
* @param A not modified.
*/
@Override
public boolean setA(DMatrixRMaj A) {
if( A.numRows > maxRows || A.numCols > maxCols ) {
setMaxSize(A.numRows,A.numCols);
}
_setA(A);
if( !decomposer.decompose(A) )
return false;
Q.reshape(numRows,numRows, false);
R.reshape(numRows,numCols, false);
decomposer.getQ(Q,false);
decomposer.getR(R,false);
return true;
}
@Override
public /**/double quality() {
return SpecializedOps_DDRM.qualityTriangular(R);
}
/**
* Solves for X using the QR decomposition.
*
* @param B A matrix that is n by m. Not modified.
* @param X An n by m matrix where the solution is written to. Modified.
*/
@Override
public void solve(DMatrixRMaj B, DMatrixRMaj X) {
if( B.numRows != numRows )
throw new IllegalArgumentException("Unexpected dimensions for X: X rows = "+X.numRows+" expected = "+numCols);
X.reshape(numCols,B.numCols);
int BnumCols = B.numCols;
Y.reshape(numRows,1, false);
Z.reshape(numRows,1, false);
// solve each column one by one
for( int colB = 0; colB < BnumCols; colB++ ) {
// make a copy of this column in the vector
for( int i = 0; i < numRows; i++ ) {
Y.data[i] = B.get(i,colB);
}
// Solve Qa=b
// a = Q'b
CommonOps_DDRM.multTransA(Q,Y,Z);
// solve for Rx = b using the standard upper triangular solver
TriangularSolver_DDRM.solveU(R.data,Z.data,numCols);
// save the results
for( int i = 0; i < numCols; i++ ) {
X.set(i,colB,Z.data[i]);
}
}
}
@Override
public boolean modifiesA() {
return decomposer.inputModified();
}
@Override
public boolean modifiesB() {
return false;
}
@Override
public QRDecomposition getDecomposition() {
return decomposer;
}
public QRDecomposition getDecomposer() {
return decomposer;
}
public DMatrixRMaj getQ() {
return Q;
}
public DMatrixRMaj getR() {
return R;
}
}