/*
* Copyright 2011-2013, by Vladimir Kostyukov and Contributors.
*
* This file is part of la4j project (http://la4j.org)
*
* 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.
*
* Contributor(s): Maxim Samoylov
*
*/
package org.la4j.linear;
import org.la4j.LinearAlgebra;
import org.la4j.Matrix;
import org.la4j.Vector;
/**
* This class provides solution of "fat" linear system with least euclidean norm.
* See details
* <p>
* <a href="http://see.stanford.edu/materials/lsoeldsee263/08-min-norm.pdf">here.</a>
* </p>
*/
public class LeastNormSolver extends AbstractSolver implements LinearSystemSolver {
protected LeastNormSolver(Matrix a) {
super(a);
}
@Override
public Vector solve(Vector b) {
ensureRHSIsCorrect(b);
Matrix temp = self().multiply(self().rotate());
Matrix pseudoInverse = self().rotate().multiply(temp.withInverter(LinearAlgebra.InverterFactory.GAUSS_JORDAN).inverse());
return pseudoInverse.multiply(b);
}
@Override
public boolean applicableTo(Matrix matrix) {
//TODO: we need to think about how to improve the speed here.
//Note: Matrix.rank() works for O(N^3) which is quite slow.
int r = matrix.rank();
return (r == matrix.rows() || r == matrix.columns());
}
}