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Title: Large Scale Non-Linear Programming for PDE Constrained Optimization

Abstract

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.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Sandia National Labs., Albuquerque, NM (US); Sandia National Labs., Livermore, CA (US)
Sponsoring Org.:
US Department of Energy (US)
OSTI Identifier:
805833
Report Number(s):
SAND2002-3198
TRN: US200303%%268
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Resource Relation:
Other Information: PBD: 1 Oct 2002
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGORITHMS; DESIGN; FLUID FLOW; PROGRAMMING; COMPUTERIZED SIMULATION; SIMULATORS; CHEMICAL VAPOR DEPOSITION

Citation Formats

VAN BLOEMEN WAANDERS, BART G, BARTLETT, ROSCOE A, LONG, KEVIN R, BOGGS, PAUL T, and SALINGER, ANDREW G. Large Scale Non-Linear Programming for PDE Constrained Optimization. United States: N. p., 2002. Web. doi:10.2172/805833.
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. United States. doi:10.2172/805833.
VAN BLOEMEN WAANDERS, BART G, BARTLETT, ROSCOE A, LONG, KEVIN R, BOGGS, PAUL T, and SALINGER, ANDREW G. Tue . "Large Scale Non-Linear Programming for PDE Constrained Optimization". United States. doi:10.2172/805833. https://www.osti.gov/servlets/purl/805833.
@article{osti_805833,
title = {Large Scale Non-Linear Programming for PDE Constrained Optimization},
author = {VAN BLOEMEN WAANDERS, BART G and BARTLETT, ROSCOE A and LONG, KEVIN R and BOGGS, PAUL T and SALINGER, ANDREW G},
abstractNote = {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.},
doi = {10.2172/805833},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2002},
month = {10}
}

Technical Report:

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