Optimization and Systems Theory Seminar
Friday, September 8, 2006, 11.00-12.00, Room 3721, Lindstedtsvägen 25

Josh Griffin
Sandia National Labs
Livermore, California
E-mail: jgriffi@sandia.gov

A parallel, asynchronous method for derivative-free nonlinear programs

A strong need for derivative-free algorithms exists in the context of real-world optimization problems where function evaluations can be computationally expensive and noisy. The objective and constraint functions commonly exist as simple script interfaces to CPU intensive model analysis software. A single evaluation may involve invoking cumbersome simulation codes whose run time is measured in hours. In this context, we present an asynchronous parallel implementation of a derivative-free augmented Lagrangian algorithm for handling general nonlinear constraints. The method requires approximate minimizers to a series of linearly constrained subproblems involving the augmented Lagrangian of the nonlinear constraints. These subproblem are solved using a generating set search algorithm capable of handling degenerate linear constraints. The objective and nonlinear constraint functions are computed asynchronously in parallel.
A description and theoretical analysis of the algorithm will be given followed by numerical results.

Calendar of seminars
Last update: August 16, 2006 by Marie Lundin.