SIMANN: A Global Optimization Algorithm using Simulated Annealing
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.
Volume (Year): 1 (1996)
Issue (Month): 3 (October)
|Contact details of provider:|| Web page: http://www.degruyter.com|
|Order Information:||Web: http://www.degruyter.com/view/j/snde|
When requesting a correction, please mention this item's handle: RePEc:bpj:sndecm:v:1:y:1996:i:3:n:al1. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Peter Golla)
If references are entirely missing, you can add them using this form.