Java tutorial
/* * 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.analysis; /** * Extension of {@link MultivariateRealFunction} representing a differentiable * multivariate real function. * @version $Revision: 811685 $ $Date: 2009-09-05 19:36:48 +0200 (sam. 05 sept. 2009) $ * @since 2.0 */ public interface DifferentiableMultivariateRealFunction extends MultivariateRealFunction { /** * Returns the partial derivative of the function with respect to a point coordinate. * <p> * The partial derivative is defined with respect to point coordinate * x<sub>k</sub>. If the partial derivatives with respect to all coordinates are * needed, it may be more efficient to use the {@link #gradient()} method which will * compute them all at once. * </p> * @param k index of the coordinate with respect to which the partial * derivative is computed * @return the partial derivative function with respect to k<sup>th</sup> point coordinate */ MultivariateRealFunction partialDerivative(int k); /** * Returns the gradient function. * <p>If only one partial derivative with respect to a specific coordinate is * needed, it may be more efficient to use the {@link #partialDerivative(int)} method * which will compute only the specified component.</p> * @return the gradient function */ MultivariateVectorialFunction gradient(); }