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Programme to "Global Optimization of Statistical Functions with Simulated Annealing"

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Author Info

  • William L. Goffe

    ()
    (University of Southern Mississippi)

  • Gary D. Ferrier
  • John Rogers

Abstract

Simulated annealing is a global optimization method that distinguishes between different local optima. Starting from an initial point, the algorithm takes a step and the function is evaluated. When minimizing a function, any downhill step is accepted and the process repeats from this new point. An uphill step may be accepted. Thus, it can escape from local optima. This uphill decision is made by the Metropolis criteria. As the optimization process proceeds, the length of the steps decline and the algorithm closes in on the global optimum. Since the algorithm makes very few assumptions regarding the function to be optimized, it is quite robust with respect to non-quadratic surfaces. The degree of robustness can be adjusted by the user. In fact, simulated annealing can be used as a local optimizer for difficult functions. This implementation of simulated annealing was used in "Global Optimization of Statistical Functions with Simulated Annealing," Goffe, Ferrier and Rogers, Journal of Econometrics, vol. 60, no. 1/2, Jan./Feb. 1994, pp. 65-100. Briefly, we found it competitive, if not superior, to multiple restarts of conventional optimization routines for difficult optimization problems.

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File URL: ftp://ftp.repec.org/RePEc/cod/html/Fortran/SimAnnealing.si
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File URL: ftp://ftp.repec.org/RePEc/cod/html/Fortran/SimAnnealing.f
File Function: Simulated Annealing source code
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Bibliographic Info

Software component provided by in its series Fortran codes with number simanneal.

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Programming language: Fortran
Requires: Fortran compiler
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Handle: RePEc:cod:fortra:simanneal

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