All Downloads are FREE. Search and download functionalities are using the official Maven repository.

org.apache.commons.math3.optim.linear.LinearObjectiveFunction Maven / Gradle / Ivy

Go to download

The Apache Commons Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.

There is a newer version: 3.6.1
Show newest version
/*
 * 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.optim.linear;

import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.io.Serializable;
import org.apache.commons.math3.analysis.MultivariateFunction;
import org.apache.commons.math3.linear.MatrixUtils;
import org.apache.commons.math3.linear.RealVector;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.optim.OptimizationData;

/**
 * An objective function for a linear optimization problem.
 * 

* A linear objective function has one the form: *

 * c1x1 + ... cnxn + d
 * 
* The ci and d are the coefficients of the equation, * the xi are the coordinates of the current point. *

* * @since 2.0 */ public class LinearObjectiveFunction implements MultivariateFunction, OptimizationData, Serializable { /** Serializable version identifier. */ private static final long serialVersionUID = -4531815507568396090L; /** Coefficients of the linear equation (ci). */ private final transient RealVector coefficients; /** Constant term of the linear equation. */ private final double constantTerm; /** * @param coefficients Coefficients for the linear equation being optimized. * @param constantTerm Constant term of the linear equation. */ public LinearObjectiveFunction(double[] coefficients, double constantTerm) { this(new ArrayRealVector(coefficients), constantTerm); } /** * @param coefficients Coefficients for the linear equation being optimized. * @param constantTerm Constant term of the linear equation. */ public LinearObjectiveFunction(RealVector coefficients, double constantTerm) { this.coefficients = coefficients; this.constantTerm = constantTerm; } /** * Gets the coefficients of the linear equation being optimized. * * @return coefficients of the linear equation being optimized. */ public RealVector getCoefficients() { return coefficients; } /** * Gets the constant of the linear equation being optimized. * * @return constant of the linear equation being optimized. */ public double getConstantTerm() { return constantTerm; } /** * Computes the value of the linear equation at the current point. * * @param point Point at which linear equation must be evaluated. * @return the value of the linear equation at the current point. */ public double value(final double[] point) { return value(new ArrayRealVector(point, false)); } /** * Computes the value of the linear equation at the current point. * * @param point Point at which linear equation must be evaluated. * @return the value of the linear equation at the current point. */ public double value(final RealVector point) { return coefficients.dotProduct(point) + constantTerm; } @Override public boolean equals(Object other) { if (this == other) { return true; } if (other instanceof LinearObjectiveFunction) { LinearObjectiveFunction rhs = (LinearObjectiveFunction) other; return (constantTerm == rhs.constantTerm) && coefficients.equals(rhs.coefficients); } return false; } @Override public int hashCode() { return Double.valueOf(constantTerm).hashCode() ^ coefficients.hashCode(); } /** * Serialize the instance. * @param oos stream where object should be written * @throws IOException if object cannot be written to stream */ private void writeObject(ObjectOutputStream oos) throws IOException { oos.defaultWriteObject(); MatrixUtils.serializeRealVector(coefficients, oos); } /** * Deserialize the instance. * @param ois stream from which the object should be read * @throws ClassNotFoundException if a class in the stream cannot be found * @throws IOException if object cannot be read from the stream */ private void readObject(ObjectInputStream ois) throws ClassNotFoundException, IOException { ois.defaultReadObject(); MatrixUtils.deserializeRealVector(this, "coefficients", ois); } }




© 2015 - 2024 Weber Informatics LLC | Privacy Policy