Efficient Global Optimization Under Conditions of Noise and Uncertainty - A Multi-Model Multi-Grid Windowing Approach

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Description

Incomplete convergence in numerical simulation such as computational physics simulations and/or Monte Carlo simulations can enter into the calculation of the objective function in an optimization problem, producing noise, bias, and topo- graphical inaccuracy in the objective function. These affect accuracy and convergence rate in the optimization problem. This paper is concerned with global searching of a diverse parameter space, graduating to accelerated local convergence to a (hopefully) global optimum, in a framework that acknowledges convergence uncertainty and manages model resolu- tion to efficiently reduce uncertainty in the final optimum. In its own right, the global-to-local optimization engine employed here ... continued below

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6 Pages

Creation Information

Romero, Vicente J. May 18, 1999.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA
    Place of Publication: Albuquerque, New Mexico

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Description

Incomplete convergence in numerical simulation such as computational physics simulations and/or Monte Carlo simulations can enter into the calculation of the objective function in an optimization problem, producing noise, bias, and topo- graphical inaccuracy in the objective function. These affect accuracy and convergence rate in the optimization problem. This paper is concerned with global searching of a diverse parameter space, graduating to accelerated local convergence to a (hopefully) global optimum, in a framework that acknowledges convergence uncertainty and manages model resolu- tion to efficiently reduce uncertainty in the final optimum. In its own right, the global-to-local optimization engine employed here (devised for noise tolerance) performs better than other classical and contemporary optimization approaches tried individually and in combination on the "industrial" test problem to be presented.

Physical Description

6 Pages

Source

  • 3rd World Conference of Structural and Multidisciplinary Optimization; Buffalo, NY; 05/17-21/1999

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  • Other: DE00007256
  • Report No.: SAND99-1244C
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 7256
  • Archival Resource Key: ark:/67531/metadc703138

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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Creation Date

  • May 18, 1999

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

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  • Nov. 23, 2016, 5:34 p.m.

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Romero, Vicente J. Efficient Global Optimization Under Conditions of Noise and Uncertainty - A Multi-Model Multi-Grid Windowing Approach, article, May 18, 1999; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc703138/: accessed December 10, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.