org.ejml.dense.row.linsol.qr.LinearSolverQrpHouseCol_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.decomposition.TriangularSolver_DDRM;
import org.ejml.dense.row.decomposition.qr.QRColPivDecompositionHouseholderColumn_DDRM;
import org.ejml.dense.row.decomposition.qr.QrHelperFunctions_DDRM;
/**
*
* Performs a pseudo inverse solver using the {@link org.ejml.dense.row.decomposition.qr.QRColPivDecompositionHouseholderColumn_DDRM} decomposition
* directly. For details on how the pseudo inverse is computed see {@link BaseLinearSolverQrp_DDRM}.
*
*
* @author Peter Abeles
*/
public class LinearSolverQrpHouseCol_DDRM extends BaseLinearSolverQrp_DDRM {
// Computes the QR decomposition
private QRColPivDecompositionHouseholderColumn_DDRM decomposition;
// storage for basic solution
private DMatrixRMaj x_basic = new DMatrixRMaj(1,1);
public LinearSolverQrpHouseCol_DDRM(QRColPivDecompositionHouseholderColumn_DDRM decomposition,
boolean norm2Solution)
{
super(decomposition,norm2Solution);
this.decomposition = decomposition;
}
@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;
// get the pivots and transpose them
int pivots[] = decomposition.getColPivots();
double qr[][] = decomposition.getQR();
double gammas[] = decomposition.getGammas();
// solve each column one by one
for( int colB = 0; colB < BnumCols; colB++ ) {
x_basic.reshape(numRows, 1);
Y.reshape(numRows,1);
// make a copy of this column in the vector
for( int i = 0; i < numRows; i++ ) {
x_basic.data[i] = B.get(i,colB);
}
// Solve Q*x=b => x = Q'*b
// Q_n*b = (I-gamma*u*u^T)*b = b - u*(gamma*U^T*b)
for( int i = 0; i < rank; i++ ) {
double u[] = qr[i];
double vv = u[i];
u[i] = 1;
QrHelperFunctions_DDRM.rank1UpdateMultR(x_basic, u, gammas[i], 0, i, numRows, Y.data);
u[i] = vv;
}
// solve for Rx = b using the standard upper triangular solver
TriangularSolver_DDRM.solveU(R11.data, x_basic.data, rank);
// finish the basic solution by filling in zeros
x_basic.reshape(numCols, 1, true);
for( int i = rank; i < numCols; i++)
x_basic.data[i] = 0;
if( norm2Solution && rank < numCols )
upgradeSolution(x_basic);
// save the results
for( int i = 0; i < numCols; i++ ) {
X.set(pivots[i],colB,x_basic.data[i]);
}
}
}
@Override
public boolean modifiesA() {
return decomposition.inputModified();
}
@Override
public boolean modifiesB() {
return false;
}
}