| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w26340 |
| 来源ID | Working Paper 26340 |
| Benchmarking Global Optimizers | |
| Antoine Arnoud; Fatih Guvenen; Tatjana Kleineberg | |
| 发表日期 | 2019-10-07 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | 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. |
| 主题 | Econometrics ; Estimation Methods ; Microeconomics ; Mathematical Tools ; General Equilibrium ; Labor Economics ; Labor Compensation |
| URL | https://www.nber.org/papers/w26340 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/584011 |
| 推荐引用方式 GB/T 7714 | Antoine Arnoud,Fatih Guvenen,Tatjana Kleineberg. Benchmarking Global Optimizers. 2019. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w26340.pdf（3161KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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