org.ejml.dense.row.linsol.qr.SolvePseudoInverseQrp_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.decomposition.TriangularSolver_DDRM;
import org.ejml.interfaces.decomposition.QRPDecomposition_F64;
/**
*
* A pseudo inverse solver for a generic QR column pivot decomposition algorithm. See
* {@link BaseLinearSolverQrp_DDRM} for technical details on the algorithm.
*
*
* @author Peter Abeles
*/
public class SolvePseudoInverseQrp_DDRM extends BaseLinearSolverQrp_DDRM {
// stores the orthogonal Q matrix from QR decomposition
private DMatrixRMaj Q=new DMatrixRMaj(1,1);
// storage for basic solution
private DMatrixRMaj x_basic =new DMatrixRMaj(1,1);
/**
* Configure and provide decomposition
*
* @param decomposition Decomposition used.
* @param norm2Solution If true the basic solution will be returned, false the minimal 2-norm solution.
*/
public SolvePseudoInverseQrp_DDRM(QRPDecomposition_F64 decomposition,
boolean norm2Solution) {
super(decomposition,norm2Solution);
}
@Override
public boolean setA(DMatrixRMaj A) {
if( !super.setA(A))
return false;
Q.reshape(A.numRows, A.numRows);
decomposition.getQ(Q, false);
return true;
}
@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();
// 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++ ) {
Y.data[i] = B.get(i,colB);
}
// Solve Q*a=b => a = Q'*b
CommonOps_DDRM.multTransA(Q, Y, x_basic);
// 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;
}
}