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.math3.fitting.leastsquares; /** * An algorithm that can be applied to a non-linear least squares problem. * * @since 3.3 */ public interface LeastSquaresOptimizer { /** * Solve the non-linear least squares problem. * * * @param leastSquaresProblem the problem definition, including model function and * convergence criteria. * @return The optimum. */ Optimum optimize(LeastSquaresProblem leastSquaresProblem); /** * The optimum found by the optimizer. This object contains the point, its value, and * some metadata. */ //TODO Solution? interface Optimum extends LeastSquaresProblem.Evaluation { /** * Get the number of times the model was evaluated in order to produce this * optimum. * * @return the number of model (objective) function evaluations */ int getEvaluations(); /** * Get the number of times the algorithm iterated in order to produce this * optimum. In general least squares it is common to have one {@link * #getEvaluations() evaluation} per iterations. * * @return the number of iterations */ int getIterations(); } }