Experience with approximations in the trust-region parallel direct search algorithm

S. M. Shontz, V. E. Howle, P. D. Hough

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Recent years have seen growth in the number of algorithms designed to solve challenging simulation-based nonlinear optimization problems. One such algorithm is the Trust-Region Parallel Direct Search (TRPDS) method developed by Hough and Meza. In this paper, we take advantage of the theoretical properties of TRPDS to make use of approximation models in order to reduce the computational cost of simulation-based optimization. We describe the extension, which we call mTRPDS, and present the results of a case study for two earth penetrator design problems. In the case study, we conduct computational experiments with an array of approximations within the mTRPDS algorithm and compare the numerical results to the original TRPDS algorithm and a trust-region method implemented using the speculative gradient approach described by Byrd, Schnabel, and Shultz. The results suggest new ways to improve the algorithm.

Original languageEnglish
Title of host publicationComputational Science - ICCS 2009 - 9th International Conference, Proceedings
Number of pages10
EditionPART 1
StatePublished - 2009
Event9th International Conference on Computational Science, ICCS 2009 - Baton Rouge, LA, United States
Duration: May 25 2009May 27 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5544 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Conference on Computational Science, ICCS 2009
Country/TerritoryUnited States
CityBaton Rouge, LA


  • Approximation models
  • Nonlinear programming
  • Parallel optimization


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