A Toolkit for Optimizing Functions in Economics
Optimization algorithms must be among the most common numerical methods used by economists. Yet, there is surprisingly little guidance on choosing the appropriate one. This problem is most notable with regard to conventional versus global optimizers. Typically, a global optimizer is used when a conventional one fails after substantial ``fiddling'' with a conventional optimizer. This paper introduces three different, easy-to-use, tools (cross-sections, radius plots, and a measure of the non-quadratic behavior of a function) that are designed to indicate when a global optimizer is needed. With their use, researchers should spend less time fiddling and more time generating results.
|Date of creation:||16 Jul 1997|
|Date of revision:|
|Note:||Type of Document - ; prepared on Linux/LaTeX; to print on PostScript; pages: 14; figures: included. none|
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- Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
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"Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features,"
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American Statistical Association, vol. 13(1), pages 53-66, January.
- Michael B. Gordy, . "GA.M: A Matlab routine for function maximization using a Genetic Algorithm," Matlab codes ga, , revised 12 Feb 1996.
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