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Java example source code file (Preconditioner.java)

This example Java source code file (Preconditioner.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

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Java - Java tags/keywords

deprecated, preconditioner

The Preconditioner.java Java example source code

 * 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,
 * See the License for the specific language governing permissions and
 * limitations under the License.

package org.apache.commons.math3.optimization.general;

 * This interface represents a preconditioner for differentiable scalar
 * objective function optimizers.
 * @deprecated As of 3.1 (to be removed in 4.0).
 * @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>-1r where H is the objective function hessian
    double[] precondition(double[] point, double[] r);

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