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SIMANN: A Global Optimization Algorithm using Simulated Annealing

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Author Info
William Goffe (University of Southern Mississippi)

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Abstract

This paper describes SIMANN, a Fortran and GAUSS implementation of the simulated annealing algorithm. The Fortran code was used in "Global Optimization of Statistical Functions with Simulated Annealing" (Goffe, Ferrier, and Rogers 1994). In that paper, simulated annealing was found to be competitive, if not superior, to multiple restarts of conventional optimization routines for difficult optimization problems. This paper compares SIMANN to the DFP algorithm on another optimization problem, namely, the maximum likelihood estimation of a rational expectations model, which was previously studied in the literature. SIMANN again performs quite well, and shows several advantages over DFP. This paper also describes simulated annealing, and gives explicit directions and an example for using the included GAUSS and Fortran code.

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Publisher Info
Article provided by Berkeley Electronic Press in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 1 (1996)
Issue (Month): 3 ()
Pages: 169-176
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Handle: RePEc:bep:sndecm:1:1996:3:169-176

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Keywords: optimization algorithm simulated annealing numerically intensive

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. 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. [Downloadable!] (restricted)
  2. Veall, Michael R, 1990. "Testing for a Global Maximum in an Econometric Context," Econometrica, Econometric Society, vol. 58(6), pages 1459-65, November. [Downloadable!] (restricted)
  3. Hoffman, Dennis L. & Schmidt, Peter, 1981. "Testing the restrictions implied by the rational expectations hypothesis," Journal of Econometrics, Elsevier, vol. 15(2), pages 265-287, February. [Downloadable!] (restricted)
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(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. André Kurmann, 2004. "Maximum Likelihood Estimation of Dynamic Stochastic Theories with an Application to New Keynesian Pricing," Macroeconomics 0409028, EconWPA. [Downloadable!]
    Other versions:
  2. Schündeln, Matthias, 2005. "Modeling Firm Dynamics to Identify the Cost of Financing Constraints in Ghanaian Manufacturing," Proceedings of the German Development Economics Conference, Kiel 2005 29, Verein für Socialpolitik, Research Committee Development Economics. [Downloadable!]
  3. William L. Goffe, 1997. "A Toolkit for Optimizing Functions in Economics," Computational Economics 9707001, EconWPA. [Downloadable!]
    Other versions:
  4. Günter Coenen & Giovanni Lombardo & Frank Smets & Roland Straub, 2008. "International transmission and monetary policy cooperation," Working Paper Series 858, European Central Bank. [Downloadable!]
  5. Teruo Nakatsuma, 1998. "A Markov-Chain Sampling Algorithm for GARCH Models," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 3(2), pages 107-117. [Downloadable!] (restricted)
  6. Roger A. McCain, 2000. "Road Rage: Imitative Learning Of Self-Destructive Behavior In An Agent-Based Simulation," Computing in Economics and Finance 2000 270, Society for Computational Economics. [Downloadable!]
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