Global Optimization of Statistical Functions with Simulated Annealing
Document Type
Article
Publication Date
1-1-1994
Department
Management and International Business
Abstract
Many statistical methods rely on numerical optimization to estimate a model's parameters. Unfortunately, conventional algorithms sometimes fail. Even when they do converge, there is no assurance that they have found the global, rather than a local, optimum. We test a new optimization algorithm, simulated annealing, on four econometric problems and compare it to three common conventional algorithms. Not only can simulated annealing find the global optimum, it is also less likely to fail on difficult functions because it is a very robust algorithm. The promise of simulated annealing is demonstrated on the four econometric problems.
Publication Title
Journal of Econometrics
Volume
60
Issue
1-2
First Page
65
Last Page
99
Recommended Citation
Goffe, W. L.,
Ferrier, G. D.,
Rogers, J.
(1994). Global Optimization of Statistical Functions with Simulated Annealing. Journal of Econometrics, 60(1-2), 65-99.
Available at: https://aquila.usm.edu/fac_pubs/7164