A class of trust-region methods for parallel optimization

Hough, P D; Meza, J C

doi:10.2172/751035

Title: A class of trust-region methods for parallel optimization

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Abstract

The authors present a new class of optimization methods that incorporates a Parallel Direct Search (PDS) method within a trust-region Newton framework. This approach combines the inherent parallelism of PDS with the rapid and robust convergence properties of Newton methods. Numerical tests have yielded favorable results for both standard test problems and engineering applications. In addition, the new method appears to be more robust in the presence of noisy functions that are inherent in many engineering simulations.

Authors:: Hough, P D; Meza, J C

Publication Date:: Mon Mar 01 00:00:00 EST 1999

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)

Sponsoring Org.:: US Department of Energy (US)

OSTI Identifier:: 751035

Report Number(s):: SAND98-8245
TRN: AH200020%%91

DOE Contract Number:: AC04-94AL85000

Resource Type:: Technical Report

Resource Relation:: Other Information: PBD: 1 Mar 1999

Country of Publication:: United States

Language:: English

Subject:: 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; PARALLEL PROCESSING; NONLINEAR PROGRAMMING; NEWTON METHOD; PERFORMANCE; OPTIMIZATION; PARALLEL OPTIMIZATION; DIRECT SEARCH METHODS, NONLINEAR PROGRAMMING

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                    Hough, P D, and Meza, J C. A class of trust-region methods for parallel optimization.  United States: N. p., 1999. 
        Web.  doi:10.2172/751035.

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                    Hough, P D, & Meza, J C. A class of trust-region methods for parallel optimization.  United States.  https://doi.org/10.2172/751035

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                    Hough, P D, and Meza, J C. 1999.  
        "A class of trust-region methods for parallel optimization".  United States.  https://doi.org/10.2172/751035.  https://www.osti.gov/servlets/purl/751035.

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@article{osti_751035,

  title        = {A class of trust-region methods for parallel optimization},

  author       = {Hough, P D and Meza, J C},

  abstractNote = {The authors present a new class of optimization methods that incorporates a Parallel Direct Search (PDS) method within a trust-region Newton framework. This approach combines the inherent parallelism of PDS with the rapid and robust convergence properties of Newton methods. Numerical tests have yielded favorable results for both standard test problems and engineering applications. In addition, the new method appears to be more robust in the presence of noisy functions that are inherent in many engineering simulations.},

  doi          = {10.2172/751035},

  url          = {https://www.osti.gov/biblio/751035},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Mar 01 00:00:00 EST 1999},

  month        = {Mon Mar 01 00:00:00 EST 1999}

}

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