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DDogleg Numerics is a high performance Java library for non-linear optimization, robust model fitting, polynomial root finding, sorting, and more.
/*
* Copyright (c) 2012-2018, Peter Abeles. All Rights Reserved.
*
* This file is part of DDogleg (http://ddogleg.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.
*/
package org.ddogleg.optimization.lm;
import org.ddogleg.optimization.UnconstrainedLeastSquaresSchur;
import org.ddogleg.optimization.functions.FunctionNtoM;
import org.ddogleg.optimization.functions.SchurJacobian;
import org.ddogleg.optimization.math.HessianSchurComplement;
import org.ddogleg.optimization.math.MatrixMath;
import org.ejml.data.DMatrix;
import org.ejml.data.DMatrixRMaj;
import javax.annotation.Nonnull;
/**
* Implementation of {@link LevenbergMarquardt_F64} for {@link UnconstrainedLeastSquaresSchur}.
*
* @author Peter Abeles
*/
public class UnconLeastSqLevenbergMarquardtSchur_F64
extends LevenbergMarquardt_F64>
implements UnconstrainedLeastSquaresSchur
{
// Left and right side of the jacobian matrix
protected S jacLeft;
protected S jacRight;
protected FunctionNtoM functionResiduals;
protected SchurJacobian functionJacobian;
public UnconLeastSqLevenbergMarquardtSchur_F64(MatrixMath math,
HessianSchurComplement hessian )
{
super(math,hessian);
this.jacLeft = math.createMatrix();
this.jacRight = math.createMatrix();
}
@Override
public void setFunction(FunctionNtoM function, @Nonnull SchurJacobian jacobian) {
this.functionResiduals = function;
this.functionJacobian = jacobian;
}
@Override
public void initialize(double[] initial, double ftol, double gtol) {
config.ftol = ftol;
config.gtol = gtol;
super.initialize(initial,
functionResiduals.getNumOfInputsN(),
functionResiduals.getNumOfOutputsM());
}
@Override
public double[] getParameters() {
return x.data;
}
@Override
public double getFunctionValue() {
return fx;
}
@Override
public boolean isUpdated() {
return mode == Mode.COMPUTE_DERIVATIVES;
}
@Override
public boolean isConverged() {
return mode == Mode.CONVERGED;
}
@Override
protected void functionGradientHessian(DMatrixRMaj x, boolean sameStateAsResiduals,
DMatrixRMaj gradient, HessianSchurComplement hessian) {
if( !sameStateAsResiduals )
functionResiduals.process(x.data,residuals.data);
functionJacobian.process(x.data,jacLeft,jacRight);
hessian.computeHessian(jacLeft,jacRight);
hessian.computeGradient(jacLeft,jacRight,residuals,gradient);
}
@Override
protected void computeResiduals(DMatrixRMaj x, DMatrixRMaj residuals) {
functionResiduals.process(x.data,residuals.data);
}
}