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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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.apache.commons.math3.optim.nonlinear.scalar.gradient;
/**
* This interface represents a preconditioner for differentiable scalar
* objective function optimizers.
* @since 2.0
*/
public interface Preconditioner {
/**
* Precondition a search direction.
* <p>
* The returned preconditioned search direction must be computed fast or
* the algorithm performances will drop drastically. A classical approach
* is to compute only the diagonal elements of the hessian and to divide
* the raw search direction by these elements if they are all positive.
* If at least one of them is negative, it is safer to return a clone of
* the raw search direction as if the hessian was the identity matrix. The
* rationale for this simplified choice is that a negative diagonal element
* means the current point is far from the optimum and preconditioning will
* not be efficient anyway in this case.
* </p>
* @param point current point at which the search direction was computed
* @param r raw search direction (i.e. opposite of the gradient)
* @return approximation of H<sup>-1</sup>r where H is the objective function hessian
*/
double[] precondition(double[] point, double[] r);
}
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