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Benchmarking Global Optimizers

Author

Listed:
  • Antoine Arnoud
  • Fatih Guvenen
  • Tatjana Kleineberg

Abstract

We benchmark six global optimization algorithms by comparing their performance on challenging multidimensional test functions as well as on a method of simulated moments estimation of a panel data model of earnings dynamics. Five of the algorithms are from the popular NLopt open-source library: (i) Controlled Random Search with local mutation (CRS), (ii) Improved Stochastic Ranking Evolution Strategy (ISRES), (iii) Multi-Level Single-Linkage (MLSL), (iv) Stochastic Global Optimization (StoGo), and (v) Evolutionary Strategy with Cauchy distribution (ESCH). The sixth algorithm is TikTak, which is a multistart global optimization algorithm used in some recent economic applications. For completeness, we add three popular local algorithms to the comparison—the Nelder-Mead downhill simplex algorithm, the Derivative-Free Nonlinear Least Squares (DFNLS) algorithm, and a popular variant of the Davidon-Fletcher-Powell (DFPMIN) algorithm. To give a detailed comparison of algorithms, we use benchmarking tools recently developed in the optimization literature. We find that the success rate of many optimizers varies dramatically with the characteristics of each problem and the computational budget that is available. Overall, TikTak is the strongest performer both on the test functions and the economic application. The next-best performing optimizers are StoGo for the test functions and MLSL and ISRES for the economic application.

Suggested Citation

  • Antoine Arnoud & Fatih Guvenen & Tatjana Kleineberg, 2023. "Benchmarking Global Optimizers," Working Papers 801, Federal Reserve Bank of Minneapolis.
    • Handle: RePEc:fip:fedmwp:97507
    DOI: 10.21034/wp.801
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    References listed on IDEAS

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    1. P. Kaelo & M. M. Ali, 2006. "Some Variants of the Controlled Random Search Algorithm for Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 130(2), pages 253-264, August.
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    More about this item

    Keywords

    Parallelized optimizer; NLopt; Calibration; Estimation; Multistart algorithms; Global optimization;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models

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