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/*
 * Ziggurat Gaussian and other randomization related algorithms.
 * Copyright 2015, 2017-2022 Vincent A. Cicirello, .
 *
 * This program 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.
 *
 * This program 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 .
 */

/**
 *
 *
 * org.cicirello.math.rand
 *
 * Copyright © 2015, 2017-2024 Vincent A.
 * Cicirello.
 *
 * 
   
 *
 * 
Support
 *
 *   
 *
 * 
See ρμ for Expanded Functionality
 *
 * If you find the functionality of this package useful, please see the ρμ library, which has absorbed this entire package and has greatly expanded
 * upon it.
 *
 * 
About
 *
 * This package originated as part of an effort to speed up the runtime of a parallel genetic
 * algorithm (PGA). The PGA in question evolved its control parameters (i.e., crossover and mutation
 * rates, etc) using Gaussian mutation. The only Gaussian implementation within the Java API is the
 * polar method (nextGaussian method of the {@link java.util.Random Random} and {@link
 * java.util.concurrent.ThreadLocalRandom ThreadLocalRandom} classes, however the polar method is
 * quite slow relative to other newer available alternatives, such as the Ziggurat method.
 *
 * 
You can find information on when this ziggurat implementation is likely advantageous,
 * including results of experiments in the following report:
 *
 * 

 *   Vincent A. Cicirello. 2024. Fast Gaussian
 *       Distributed Pseudorandom Number Generation in Java via the Ziggurat Algorithm. arXiv:2405.19493, May 2024. doi:10.48550/arXiv.2405.19493. [PDF]
 * 
 *
 * You can find some experimental data comparing the performance of a sequential genetic
 * algorithm (GA) using the implementation of the Ziggurat method for Gaussian mutation vs using the
 * more common polar method, as well as experimental data for the same comparison but with a PGA, in
 * the following paper:
 *
 * 

 *   V. A. Cicirello. Impact of Random Number Generation on Parallel Genetic Algorithms.
 *       Proceedings of the Thirty-First International Florida Artificial Intelligence Research
 *       Society Conference, pages 2-7. AAAI Press, May 2018.
 * 
 *
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
package org.cicirello.math.rand;