001 /* 002 * Licensed to the Apache Software Foundation (ASF) under one or more 003 * contributor license agreements. See the NOTICE file distributed with 004 * this work for additional information regarding copyright ownership. 005 * The ASF licenses this file to You under the Apache License, Version 2.0 006 * (the "License"); you may not use this file except in compliance with 007 * the License. You may obtain a copy of the License at 008 * 009 * http://www.apache.org/licenses/LICENSE-2.0 010 * 011 * Unless required by applicable law or agreed to in writing, software 012 * distributed under the License is distributed on an "AS IS" BASIS, 013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 014 * See the License for the specific language governing permissions and 015 * limitations under the License. 016 */ 017 018 package org.apache.commons.math3.optimization.general; 019 020 /** 021 * This interface represents a preconditioner for differentiable scalar 022 * objective function optimizers. 023 * @version $Id: Preconditioner.java 1422230 2012-12-15 12:11:13Z erans $ 024 * @deprecated As of 3.1 (to be removed in 4.0). 025 * @since 2.0 026 */ 027 @Deprecated 028 public interface Preconditioner { 029 /** 030 * Precondition a search direction. 031 * <p> 032 * The returned preconditioned search direction must be computed fast or 033 * the algorithm performances will drop drastically. A classical approach 034 * is to compute only the diagonal elements of the hessian and to divide 035 * the raw search direction by these elements if they are all positive. 036 * If at least one of them is negative, it is safer to return a clone of 037 * the raw search direction as if the hessian was the identity matrix. The 038 * rationale for this simplified choice is that a negative diagonal element 039 * means the current point is far from the optimum and preconditioning will 040 * not be efficient anyway in this case. 041 * </p> 042 * @param point current point at which the search direction was computed 043 * @param r raw search direction (i.e. opposite of the gradient) 044 * @return approximation of H<sup>-1</sup>r where H is the objective function hessian 045 */ 046 double[] precondition(double[] point, double[] r); 047 }