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Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. The library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. It includes several classes for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as common crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library. Chips-n-Salsa is customizable, making extensive use of Java's generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), as well as support for running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt to run length.

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/*
 * Chips-n-Salsa: A library of parallel self-adaptive local search algorithms.
 * Copyright (C) 2002-2022 Vincent A. Cicirello
 *
 * This file is part of Chips-n-Salsa (https://chips-n-salsa.cicirello.org/).
 *
 * Chips-n-Salsa is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Chips-n-Salsa is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see .
 */

package org.cicirello.search.representations;

import java.util.Arrays;
import org.cicirello.util.Copyable;

/**
 * A simple class for representing the parameters to a multivariate function.
 *
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public class RealVector implements RealValued, Copyable {

  private final double[] x;

  /**
   * Initializes this vector to all 0.0 values.
   *
   * @param n The length of the vector.
   */
  public RealVector(int n) {
    x = new double[n];
  }

  /**
   * Initializes the vector to the specified values.
   *
   * @param x The initial values for the vector.
   */
  public RealVector(double[] x) {
    this.x = x.clone();
  }

  /**
   * Initializes the vector as a copy of another.
   *
   * @param other The other vector to copy.
   */
  public RealVector(RealVector other) {
    x = other.x.clone();
  }

  @Override
  public final int length() {
    return x.length;
  }

  @Override
  public final double get(int i) {
    return x[i];
  }

  @Override
  public final double[] toArray(double[] values) {
    if (values == null || values.length != x.length) return x.clone();
    System.arraycopy(x, 0, values, 0, values.length);
    return values;
  }

  @Override
  public void set(int i, double value) {
    this.x[i] = value;
  }

  @Override
  public void set(double[] values) {
    System.arraycopy(values, 0, x, 0, x.length);
  }

  /**
   * Exchanges a sequence of doubles between two RealVector objects.
   *
   * @param v1 The first RealVector.
   * @param v2 The second RealVector.
   * @param firstIndex The first index of the sequence to exchange, inclusive.
   * @param lastIndex The last index of the sequence to exchange, inclusive.
   * @throws IndexOutOfBoundsException if either index is negative, or if either index ≥
   *     v1.length(), or if either index ≥ v2.length()
   */
  public static void exchange(RealVector v1, RealVector v2, int firstIndex, int lastIndex) {
    // Note: don't need to do bounds check... documented the behavior above.
    if (firstIndex > lastIndex) {
      int temp = firstIndex;
      firstIndex = lastIndex;
      lastIndex = temp;
    }
    if (canSimpleExchange(v1, v2)) {
      // Either not bounded, or bounds are the same....
      final int N = lastIndex - firstIndex + 1;
      double[] temp = new double[N];
      System.arraycopy(v1.x, firstIndex, temp, 0, N);
      System.arraycopy(v2.x, firstIndex, v1.x, firstIndex, N);
      System.arraycopy(temp, 0, v2.x, firstIndex, N);
    } else {
      // different value-bounds so need to call set to check min and max
      for (int i = firstIndex; i <= lastIndex; i++) {
        double temp = v1.x[i];
        v1.set(i, v2.x[i]);
        v2.set(i, temp);
      }
    }
  }

  private static boolean canSimpleExchange(RealVector v1, RealVector v2) {
    if (v1 instanceof BoundedRealVector) {
      return v2 instanceof BoundedRealVector
          && ((BoundedRealVector) v1).sameBounds((BoundedRealVector) v2);
    } else if (v2 instanceof BoundedRealVector) {
      return false;
    }
    return true;
  }

  /**
   * Creates an identical copy of this object.
   *
   * @return an identical copy of this object
   */
  @Override
  public RealVector copy() {
    return new RealVector(this);
  }

  /**
   * Indicates whether some other object is "equal to" this one. To be equal, the other object must
   * be of the same runtime type and contain the same values.
   *
   * @param other The other object to compare.
   * @return true if other is not null, is of the same runtime type as this, and contains the same
   *     values.
   */
  @Override
  public boolean equals(Object other) {
    if (other == null || !(other instanceof RealVector)) return false;
    return Arrays.equals(x, ((RealVector) other).x);
  }

  /**
   * Returns a hash code value for the function input object.
   *
   * @return a hash code value
   */
  @Override
  public int hashCode() {
    return Arrays.hashCode(x);
  }
}