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Simulated Annealing for Convex Optimization: Rigorous Complexity Analysis and Practical Perspectives

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  • Riley Badenbroek

    (Erasmus University Rotterdam)

  • Etienne Klerk

    (Tilburg University)

Abstract

We give a rigorous complexity analysis of the simulated annealing algorithm by Kalai and Vempala (Math Oper Res 31(2):253–266, 2006) using the type of temperature update suggested by Abernethy and Hazan (International Conference on Machine Learning, 2016). The algorithm only assumes a membership oracle of the feasible set, and we prove that it returns a solution in polynomial time which is near-optimal with high probability. Moreover, we propose a number of modifications to improve the practical performance of this method, and present some numerical results for test problems from copositive programming.

Suggested Citation

  • Riley Badenbroek & Etienne Klerk, 2022. "Simulated Annealing for Convex Optimization: Rigorous Complexity Analysis and Practical Perspectives," Journal of Optimization Theory and Applications, Springer, vol. 194(2), pages 465-491, August.
    • Handle: RePEc:spr:joptap:v:194:y:2022:i:2:d:10.1007_s10957-022-02034-x
    DOI: 10.1007/s10957-022-02034-x
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    References listed on IDEAS

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    1. de Klerk, Etienne & Laurent, Monique, 2018. "Comparison of Lasserre's measure-based bounds for polynomial optimization to bounds obtained by simulated annealing," Other publications TiSEM 78f8f496-dc89-413e-864d-f, Tilburg University, School of Economics and Management.
    2. Robert L. Smith, 1984. "Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed over Bounded Regions," Operations Research, INFORMS, vol. 32(6), pages 1296-1308, December.
    3. Etienne de Klerk & Monique Laurent, 2018. "Comparison of Lasserre’s Measure-Based Bounds for Polynomial Optimization to Bounds Obtained by Simulated Annealing," Mathematics of Operations Research, INFORMS, vol. 43(4), pages 1317-1325, November.
    4. Badenbroek, Riley & de Klerk, Etienne, 2022. "Complexity analysis of a sampling-based interior point method for convex optimization," Other publications TiSEM 3d774c6d-8141-4f31-a621-5, Tilburg University, School of Economics and Management.
    5. Claude J. P. Bélisle & H. Edwin Romeijn & Robert L. Smith, 1993. "Hit-and-Run Algorithms for Generating Multivariate Distributions," Mathematics of Operations Research, INFORMS, vol. 18(2), pages 255-266, May.
    6. Bomze, Immanuel M., 2012. "Copositive optimization – Recent developments and applications," European Journal of Operational Research, Elsevier, vol. 216(3), pages 509-520.
    7. Adam Tauman Kalai & Santosh Vempala, 2006. "Simulated Annealing for Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 31(2), pages 253-266, May.
    8. Badenbroek, Riley, 2021. "Interior point methods and simulated annealing for nonsymmetric conic optimization," Other publications TiSEM 4374ab25-fdb5-4e6e-a198-6, Tilburg University, School of Economics and Management.
    9. Wei Xia & Juan C. Vera & Luis F. Zuluaga, 2020. "Globally Solving Nonconvex Quadratic Programs via Linear Integer Programming Techniques," INFORMS Journal on Computing, INFORMS, vol. 32(1), pages 40-56, January.
    Full references (including those not matched with items on IDEAS)

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