RANK ORDERING AND POSITIVE BASES IN PATTERN SEARCH ALGORITHMS

Authors:
Robert M Lewis;Virginia Torczon
Affiliations:
-;-
Venue:
RANK ORDERING AND POSITIVE BASES IN PATTERN SEARCH ALGORITHMS
Year:
1996

Citing 0
Cited 12

Positive Bases in Numerical Optimization

Computational Optimization and Applications
Pattern Search Methods for Use-Provided Points

ICCS '01 Proceedings of the International Conference on Computational Science-Part II
References

Sourcebook of parallel computing
Grid Restrained Nelder-Mead Algorithm

Computational Optimization and Applications
Combined pattern search and ranking and selection for simulation optimization

WSC '04 Proceedings of the 36th conference on Winter simulation
Algorithm 856: APPSPACK 4.0: asynchronous parallel pattern search for derivative-free optimization

ACM Transactions on Mathematical Software (TOMS)
Optimizing an Empirical Scoring Function for Transmembrane Protein Structure Determination

INFORMS Journal on Computing
Unconstrained derivative-free optimization by successive approximation

Journal of Computational and Applied Mathematics
Combined heat and power economic dispatch by mesh adaptive direct search algorithm

Expert Systems with Applications: An International Journal
Control of dead-time systems using derivative free particle swarm optimisation

International Journal of Bio-Inspired Computation
Two minimal positive bases based direct search conjugate gradient methods for computationally expensive functions

Numerical Algorithms
Simultaneous optimization and uncertainty quantification

Journal of Computational Methods in Sciences and Engineering - Special issue on Advances in Simulation-Driven Optimization and Modeling

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present two new classes of pattern search algorithms for unconstrained minimization: the rank ordered and the positive basis pattern search methods. These algorithms can nearly halve the worst case cost of an iteration compared to the classical pattern search algorithms. The rank ordered pattern search methods are based on a heuristic for approximating the direction of steepest descent, while the positive basis pattern search methods are motivated by a generalization of the geometry characteristic of the patterns of the classical methods. We describe the new classes of algorithms and present the attendant global convergence analysis.