A class of trust-region methods for parallel optimization

Technical Report · Mon Mar 01 00:00:00 EST 1999

DOI:https://doi.org/10.2172/751035· OSTI ID:751035

Hough, P D; Meza, J C

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.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
• Sponsoring Organization: US Department of Energy (US)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 751035
• Report Number(s): SAND98-8245; TRN: AH200020%%91
• Resource Relation: Other Information: PBD: 1 Mar 1999
• Country of Publication: United States
• Language: English

Related Subjects

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