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Title: Asynchronous parallel pattern search in the context of a globally convergent augmented Lagrangian method.

Abstract

No abstract prepared.

Authors:
;
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
915171
Report Number(s):
SAND2006-1218C
TRN: US200817%%281
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the SIAM Conference on Parallel Processing for Scientific Computing held February 22-24, 2006 in San Francisco, CA.
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; LAGRANGIAN FUNCTION; PARALLEL PROCESSING; COMPUTERS

Citation Formats

Griffin, Joshua D., and Kolda, Tamara Gibson. Asynchronous parallel pattern search in the context of a globally convergent augmented Lagrangian method.. United States: N. p., 2006. Web.
Griffin, Joshua D., & Kolda, Tamara Gibson. Asynchronous parallel pattern search in the context of a globally convergent augmented Lagrangian method.. United States.
Griffin, Joshua D., and Kolda, Tamara Gibson. Wed . "Asynchronous parallel pattern search in the context of a globally convergent augmented Lagrangian method.". United States. doi:.
@article{osti_915171,
title = {Asynchronous parallel pattern search in the context of a globally convergent augmented Lagrangian method.},
author = {Griffin, Joshua D. and Kolda, Tamara Gibson},
abstractNote = {No abstract prepared.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Feb 01 00:00:00 EST 2006},
month = {Wed Feb 01 00:00:00 EST 2006}
}

Conference:
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  • Parallel pattern search (PPS) can be quite useful for engineering optimization problems characterized by a small number of variables (say 10--50) and by expensive objective function evaluations such as complex simulations that take from minutes to hours to run. However, PPS, which was originally designed for execution on homogeneous and tightly-coupled parallel machine, is not well suited to the more heterogeneous, loosely-coupled, and even fault-prone parallel systems available today. Specifically, PPS is hindered by synchronization penalties and cannot recover in the event of a failure. The authors introduce a new asynchronous and fault tolerant parallel pattern search (AAPS) method andmore » demonstrate its effectiveness on both simple test problems as well as some engineering optimization problems« less
  • In this paper the authors prove global convergence for asynchronous parallel pattern search. In standard pattern search, decisions regarding the update of the iterate and the step-length control parameter are synchronized implicitly across all search directions. They lose this feature in asynchronous parallel pattern search since the search along each direction proceeds semi-autonomously. By bounding the value of the step-length control parameter after any step that produces decrease along a single search direction, they can prove that all the processes share a common accumulation point and that such a point is a stationary point of the standard nonlinear unconstrained optimizationmore » problem.« less
  • No abstract prepared.
  • APPSPACK is software for solving unconstrained and bound constrained optimization problems. It implements an asynchronous parallel pattern search method that has been specifically designed for problems characterized by expensive function evaluations. Using APPSPACK to solve optimization problems has several advantages: No derivative information is needed; the procedure for evaluating the objective function can be executed via a separate program or script; the code can be run in serial or parallel, regardless of whether or not the function evaluation itself is parallel; and the software is freely available. We describe the underlying algorithm, data structures, and features of APPSPACK version 4.0more » as well as how to use and customize the software.« less
  • APPS implements an asynchronous and fault tolerant parallel pattern search method for optimization.