Large Scale Non-Linear Programming for PDE Constrained Optimization

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Description

Three years of large-scale PDE-constrained optimization research and development are summarized in this report. We have developed an optimization framework for 3 levels of SAND optimization and developed a powerful PDE prototyping tool. The optimization algorithms have been interfaced and tested on CVD problems using a chemically reacting fluid flow simulator resulting in an order of magnitude reduction in compute time over a black box method. Sandia's simulation environment is reviewed by characterizing each discipline and identifying a possible target level of optimization. Because SAND algorithms are difficult to test on actual production codes, a symbolic simulator (Sundance) was developed ... continued below

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291 pages

Creation Information

VAN BLOEMEN WAANDERS, BART G.; BARTLETT, ROSCOE A.; LONG, KEVIN R.; BOGGS, PAUL T. & SALINGER, ANDREW G. October 1, 2002.

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

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Description

Three years of large-scale PDE-constrained optimization research and development are summarized in this report. We have developed an optimization framework for 3 levels of SAND optimization and developed a powerful PDE prototyping tool. The optimization algorithms have been interfaced and tested on CVD problems using a chemically reacting fluid flow simulator resulting in an order of magnitude reduction in compute time over a black box method. Sandia's simulation environment is reviewed by characterizing each discipline and identifying a possible target level of optimization. Because SAND algorithms are difficult to test on actual production codes, a symbolic simulator (Sundance) was developed and interfaced with a reduced-space sequential quadratic programming framework (rSQP++) to provide a PDE prototyping environment. The power of Sundance/rSQP++ is demonstrated by applying optimization to a series of different PDE-based problems. In addition, we show the merits of SAND methods by comparing seven levels of optimization for a source-inversion problem using Sundance and rSQP++. Algorithmic results are discussed for hierarchical control methods. The design of an interior point quadratic programming solver is presented.

Physical Description

291 pages

Source

  • Other Information: PBD: 1 Oct 2002

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  • Report No.: SAND2002-3198
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/805833 | External Link
  • Office of Scientific & Technical Information Report Number: 805833
  • Archival Resource Key: ark:/67531/metadc736055

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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.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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Creation Date

  • October 1, 2002

Added to The UNT Digital Library

  • Oct. 18, 2015, 6:40 p.m.

Description Last Updated

  • April 12, 2016, 4:44 p.m.

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VAN BLOEMEN WAANDERS, BART G.; BARTLETT, ROSCOE A.; LONG, KEVIN R.; BOGGS, PAUL T. & SALINGER, ANDREW G. Large Scale Non-Linear Programming for PDE Constrained Optimization, report, October 1, 2002; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc736055/: accessed November 24, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.