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Department of Mathematics,
University of California San Diego

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Center for Computational Mathematics Seminar

Philip Gill

UCSD

A Primal-Dual Interior Method for Nonlinear Optimization

Abstract:

Interior methods provide an effective approach for the treatment of inequality constraints in nonlinearly constrained optimization. A new primal-dual interior method is proposed that has favorable global convergence properties, yet, under suitable assumptions, is equivalent to the conventional path-following interior method in the neighborhood of a solution. The method may be combined with a primal-dual shifted penalty function for the treatment of equality constraints to provide a method for general optimization problems with a mixture of equality and inequality constraints.

October 10, 2017

11:00 AM

AP&M 2402

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