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Benchmarking Global Optimizers

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  • Antoine Arnoud
  • Fatih Guvenen
  • Tatjana Kleineberg

Abstract

We benchmark seven global optimization algorithms by comparing their performance on challenging multidimensional test functions as well as a method of simulated moments estimation of a panel data model of earnings dynamics. Five of the algorithms are taken 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) algorithm, (iv) Stochastic Global Optimization (StoGo), and (v) Evolutionary Strategy with Cauchy distribution (ESCH). The other two algorithms are versions of 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 Non-linear Least Squares (DFNLS) algorithm, and a popular variant of the Davidon-Fletcher-Powell (DFPMIN) algorithm. To give a detailed comparison of algorithms, we use a set of benchmarking tools recently developed in the applied mathematics literature. We find that the success rate of many optimizers vary dramatically with the characteristics of each problem and the computational budget that is available. Overall, TikTak is the strongest performer on both the math test functions and the economic application. The next-best performing optimizers are StoGo and CRS for the test functions and MLSL for the economic application.

Suggested Citation

  • Antoine Arnoud & Fatih Guvenen & Tatjana Kleineberg, 2019. "Benchmarking Global Optimizers," NBER Working Papers 26340, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:26340
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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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    Cited by:

    1. Fella, Giulio & Audoly, Richard & De Pace, Federica, 2022. "Job Ladder, Human Capital, and the Cost of Job Loss," CEPR Discussion Papers 17746, C.E.P.R. Discussion Papers.
    2. Jean-Jacques Forneron & Liang Zhong, 2023. "Convexity Not Required: Estimation of Smooth Moment Condition Models," Papers 2304.14386, arXiv.org.
    3. Fatih Guvenen & Fatih Karahan & Serdar Ozkan & Jae Song, 2021. "What Do Data on Millions of U.S. Workers Reveal About Lifecycle Earnings Dynamics?," Econometrica, Econometric Society, vol. 89(5), pages 2303-2339, September.
    4. Fabio Blasutto, 2020. "Cohabitation vs Marriage: Mating Strategies by Education in the USA," LIDAM Discussion Papers IRES 2020023, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    5. Katrine Jakobsen & Thomas H Jørgensen & Hamish Low, 2022. "Fertility and Family Labor Supply," Economics Series Working Papers 965, University of Oxford, Department of Economics.
    6. Hassan Afrouzi & Andres Drenik & Ryan Kim, 2020. "Growing by the Masses - Revisiting the Link between Firm Size and Market Power," CESifo Working Paper Series 8633, CESifo.
    7. Rory McGee, 2021. "Old Age Savings and House Price Shocks," University of Western Ontario, Departmental Research Report Series 20214, University of Western Ontario, Department of Economics.
    8. Fabio Blasutto & Egor Kozlov, 2020. "(Changing) Marriage and Cohabitation Patterns in the US: do Divorce Laws Matter?," 2020 Papers pbl245, Job Market Papers.
    9. Cortes, Patricia & Pan, Jessica & Pilossoph, Laura & Zafar, Basit, 2021. "Gender Differences in Job Search and the Earnings Gap: Evidence from Business Majors," IZA Discussion Papers 14373, Institute of Labor Economics (IZA).
    10. Bertrand Achou, 2020. "Housing in Medicaid: Should it Really Change?," Cahiers de recherche / Working Papers 3, Institut sur la retraite et l'épargne / Retirement and Savings Institute.

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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
    • D52 - Microeconomics - - General Equilibrium and Disequilibrium - - - Incomplete Markets
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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