research-article

Model-Based Annealing Random Search with Stochastic Averaging

Authors:

Qi FanAuthors Info & Claims

ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 24, Issue 4

Article No.: 21, Pages 1 - 23

https://doi.org/10.1145/2641565

Published: 18 November 2014 Publication History

Abstract

The model-based methods have recently found widespread applications in solving hard nondifferentiable optimization problems. These algorithms are population-based and typically require hundreds of candidate solutions to be sampled at each iteration. In addition, recent convergence analysis of these algorithms also stipulates a sample size that increases polynomially with the number of iterations. In this article, we aim to improve the efficiency of model-based algorithms by reducing the number of candidate solutions generated per iteration. This is carried out through embedding a stochastic averaging procedure within these methods to make more efficient use of the past sampling information. This procedure not only can potentially reduce the number of function evaluations needed to obtain high-quality solutions, but also makes the underlying algorithms more amenable for parallel computation. The detailed implementation of our approach is demonstrated through an exemplary algorithm instantiation called Model-based Annealing Random Search with Stochastic Averaging (MARS-SA), which maintains the per iteration sample size at a small constant value. We establish the global convergence property of MARS-SA and provide numerical examples to illustrate its performance.

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Cited By

Zhao SLiu Y(2024)Numerical methods for distributed stochastic compositional optimization problems with aggregative structureOptimization Methods and Software10.1080/10556788.2024.2381214(1-32)Online publication date: 25-Jul-2024
https://doi.org/10.1080/10556788.2024.2381214
Fan QTan KZhang J(2023)Empirical tail risk management with model-based annealing random searchInsurance: Mathematics and Economics10.1016/j.insmatheco.2023.02.005110(106-124)Online publication date: May-2023
https://doi.org/10.1016/j.insmatheco.2023.02.005
Xu J(2023)Simulation Optimization: Discrete Optimization via SimulationEncyclopedia of Optimization10.1007/978-3-030-54621-2_831-1(1-6)Online publication date: 3-May-2023
https://doi.org/10.1007/978-3-030-54621-2_831-1
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Index Terms

Model-Based Annealing Random Search with Stochastic Averaging
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
    2. Mathematical optimization

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Transactions on Modeling and Computer Simulation

ACM Transactions on Modeling and Computer Simulation Volume 24, Issue 4

Special Issue on Emerging Methodologies and Applications

August 2014

132 pages

ISSN:1049-3301

EISSN:1558-1195

DOI:10.1145/2617568

Editors:
Adelinde M. Uhrmacher
University of Rostock, Germany
,
L. Jeff Hong
City University of Hong Kong, China
,
J. Q. Hu
Fudan University, China
,
Seong-Hee Kim
Georgia Institute of Technology

Issue’s Table of Contents

Copyright © 2014 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 November 2014

Accepted: 01 June 2014

Revised: 01 April 2014

Received: 01 October 2012

Published in TOMACS Volume 24, Issue 4

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Cited By

Zhao SLiu Y(2024)Numerical methods for distributed stochastic compositional optimization problems with aggregative structureOptimization Methods and Software10.1080/10556788.2024.2381214(1-32)Online publication date: 25-Jul-2024
https://doi.org/10.1080/10556788.2024.2381214
Fan QTan KZhang J(2023)Empirical tail risk management with model-based annealing random searchInsurance: Mathematics and Economics10.1016/j.insmatheco.2023.02.005110(106-124)Online publication date: May-2023
https://doi.org/10.1016/j.insmatheco.2023.02.005
Xu J(2023)Simulation Optimization: Discrete Optimization via SimulationEncyclopedia of Optimization10.1007/978-3-030-54621-2_831-1(1-6)Online publication date: 3-May-2023
https://doi.org/10.1007/978-3-030-54621-2_831-1
Zhang QHu J(2022)Actor-Critic–Like Stochastic Adaptive Search for Continuous Simulation OptimizationOperations Research10.1287/opre.2021.221470:6(3519-3537)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1287/opre.2021.2214
O'Kelly MSinha ANamkoong HDuchi JTedrake R(2018)Scalable end-to-end autonomous vehicle testing via rare-event simulationProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3327546.3327650(9849-9860)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.5555/3327546.3327650
Kiatsupaibul SSmith RZabinsky Z(2018)Single Observation Adaptive Search for Continuous Simulation OptimizationOperations Research10.1287/opre.2018.175966:6(1713-1727)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1287/opre.2018.1759
Zhou XXu M(2018)Model-Based Proposal Learning for Monte Carlo Optimization of Redundancy Allocation ProblemIEEE Access10.1109/ACCESS.2018.28677446(47953-47958)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2867744
Hsieh TEL-Manzalawy YSun YHonavar V(2018)Compositional Stochastic Average Gradient for Machine Learning and Related ApplicationsIntelligent Data Engineering and Automated Learning – IDEAL 201810.1007/978-3-030-03493-1_77(740-752)Online publication date: 21-Nov-2018
https://dl.acm.org/doi/10.1007/978-3-030-03493-1_77
Hu J(2014)Model-Based Stochastic Search MethodsHandbook of Simulation Optimization10.1007/978-1-4939-1384-8_12(319-340)Online publication date: 18-Sep-2014
https://doi.org/10.1007/978-1-4939-1384-8_12

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