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Asynchronous parallel generating set search for linearly-constrained optimization

Joshua Griffin (jgriffi***at***sandia.gov)
Tamara Kolda (tgkolda***at***sandia.gov)
R. Michael Lewis (buckaroo***at***math.wm.edu)

Abstract: Generating set search (GSS) is a family of direct search methods that encompasses generalized pattern search and related methods. We describe an algorithm for asynchronous linearly-constrained GSS, which has some complexities that make it different from both the asynchronous bound-constrained case as well as the synchronous linearly-constrained case. The algorithm has been implemented in the APPSPACK software framework and we present results from an extensive numerical study using CUTEr test problems. We discuss the results, both positive and negative, and conclude that GSS is a reliable method for solving small-to-medium sized linearly-constrained optimization problems without derivatives.

Keywords: nonlinear programming, constrained optimization, linear constraints, direct search, derivative-free optimization, generalized pattern search (GPS), generating set search (GSS), asynchronous parallel optimization, asynchronous parallel pattern search (APPS)

Category 1: Optimization Software and Modeling Systems (Parallel Algorithms )

Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )

Category 3: Robust Optimization

Citation: Technical Report SAND2006-4621, Sandia National Laboratories, Albuquerque, NM and Livermore, CA, August 2006.

Download: [PDF]

Entry Submitted: 08/10/2006
Entry Accepted: 08/10/2006
Entry Last Modified: 08/10/2006

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