Sample size selection for improved Nelder-Mead performance

Authors:
John J. Tomick;Steven F. Arnold;Russell R. Barton
Affiliations:
Department of Mathematical Sciences, U.S. Air Force Academy, Suite 6D2A, Colorado Springs, CO;Department of Statistics, The Pennsylvania State University, University Park, PA;Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA
Venue:
WSC '95 Proceedings of the 27th conference on Winter simulation
Year:
1995

Citing 7
Cited 2

Distribution of mathematical software via electronic mail

Communications of the ACM
A tutorial on simulation optimization

WSC '92 Proceedings of the 24th conference on Winter simulation
A tutorial review of techniques for simulation optimization

WSC '94 Proceedings of the 26th conference on Winter simulation
Developments in stochastic optimization algorithms with gradient approximations based on function measurements

WSC '94 Proceedings of the 26th conference on Winter simulation
Nelder-Mead simplex modifications for simulation optimization

Management Science
Testing Unconstrained Optimization Software

ACM Transactions on Mathematical Software (TOMS)
On convergence of the nelder-mead simplex algorithm for unconstrained stochastic optimization

On convergence of the nelder-mead simplex algorithm for unconstrained stochastic optimization

Simulation optimization: methods and applications

Proceedings of the 29th conference on Winter simulation
Simulation optimization: a survey of simulation optimization techniques and procedures

Proceedings of the 32nd conference on Winter simulation

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Nelder-Mead (1965) simplex algorithm has been used for sequential optimization of simulation response functions. The rescaling operations of this algorithm can lead to inappropriate termination at non-optimal points. We have used the probabilistic characterization of this behaviour to develop special rules for determining the number of replications to take for each experimental design point. Computational experiments indicate that the quality of the solution is often improved.