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.analysis; 019 020 /** 021 * Extension of {@link MultivariateRealFunction} representing a differentiable 022 * multivariate real function. 023 * @version $Revision: 811685 $ $Date: 2009-09-05 19:36:48 +0200 (sam. 05 sept. 2009) $ 024 * @since 2.0 025 */ 026 public interface DifferentiableMultivariateRealFunction extends MultivariateRealFunction { 027 028 /** 029 * Returns the partial derivative of the function with respect to a point coordinate. 030 * <p> 031 * The partial derivative is defined with respect to point coordinate 032 * x<sub>k</sub>. If the partial derivatives with respect to all coordinates are 033 * needed, it may be more efficient to use the {@link #gradient()} method which will 034 * compute them all at once. 035 * </p> 036 * @param k index of the coordinate with respect to which the partial 037 * derivative is computed 038 * @return the partial derivative function with respect to k<sup>th</sup> point coordinate 039 */ 040 MultivariateRealFunction partialDerivative(int k); 041 042 /** 043 * Returns the gradient function. 044 * <p>If only one partial derivative with respect to a specific coordinate is 045 * needed, it may be more efficient to use the {@link #partialDerivative(int)} method 046 * which will compute only the specified component.</p> 047 * @return the gradient function 048 */ 049 MultivariateVectorialFunction gradient(); 050 051 }