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A Subspace Minimization Method for Constrained Optimization
Michael Ferry
UCSD
Abstract:
We will discuss how certain properties of quasi-Newton methods have been exploited to derive an efficient algorithm for unconstrained optimization, which works by restricting search directions to a subspace at each iteration. Then we will present a new algorithm, RH-B, which applies these principles to problems with bound constraints. This will include a discussion about issues with the current implementation, suggestions for future versions and numerical results.
Tuesday, February 24, 2009
11:00AM AP&M 2402
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Directors:
Randolph E. Bank
Philip E. Gill
Michael Holst
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Randolph E. Bank
Philip E. Gill
Michael Holst
Administrative Contact:
[ Click to Send Email ]
Michael Ferry
UCSD
Abstract:
We will discuss how certain properties of quasi-Newton methods have been exploited to derive an efficient algorithm for unconstrained optimization, which works by restricting search directions to a subspace at each iteration. Then we will present a new algorithm, RH-B, which applies these principles to problems with bound constraints. This will include a discussion about issues with the current implementation, suggestions for future versions and numerical results.
Tuesday, February 24, 2009
11:00AM AP&M 2402