/*
 * 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.math.optimization.general;

import org.apache.commons.math.FunctionEvaluationException;

/**
 * This interface represents a preconditioner for differentiable scalar
 * objective function optimizers.
 * 
 * @version $Revision$ $Date$
 * @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
	 * @exception FunctionEvaluationException
	 *                if no cost can be computed for the parameters
	 * @exception IllegalArgumentException
	 *                if point dimension is wrong
	 */
	double[] precondition(double[] point, double[] r) throws FunctionEvaluationException, IllegalArgumentException;

}
