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.math.optimization.general; 019 020 import org.apache.commons.math.FunctionEvaluationException; 021 022 /** 023 * This interface represents a preconditioner for differentiable scalar 024 * objective function optimizers. 025 * @version $Revision: 782468 $ $Date: 2009-06-07 17:24:18 -0400 (Sun, 07 Jun 2009) $ 026 * @since 2.0 027 */ 028 public interface Preconditioner { 029 030 /** 031 * Precondition a search direction. 032 * <p> 033 * The returned preconditioned search direction must be computed fast or 034 * the algorithm performances will drop drastically. A classical approach 035 * is to compute only the diagonal elements of the hessian and to divide 036 * the raw search direction by these elements if they are all positive. 037 * If at least one of them is negative, it is safer to return a clone of 038 * the raw search direction as if the hessian was the identity matrix. The 039 * rationale for this simplified choice is that a negative diagonal element 040 * means the current point is far from the optimum and preconditioning will 041 * not be efficient anyway in this case. 042 * </p> 043 * @param point current point at which the search direction was computed 044 * @param r raw search direction (i.e. opposite of the gradient) 045 * @return approximation of H<sup>-1</sup>r where H is the objective function hessian 046 * @exception FunctionEvaluationException if no cost can be computed for the parameters 047 * @exception IllegalArgumentException if point dimension is wrong 048 */ 049 double[] precondition(double[] point, double[] r) 050 throws FunctionEvaluationException, IllegalArgumentException; 051 052 }