Steering Exact Penalty Methods for Optimization

Byrd Richard (richardcs.colorado.edu)
Nocedal Jorge (nocedalece.northwestern.edu)
Waltz Richard (rwaltzece.northwestern.edu)

Abstract: This paper reviews, extends and analyzes a new class of penalty methods for nonlinear optimization. These methods adjust the penalty parameter dynamically; by controlling the degree of linear feasibility achieved at every iteration, they promote balanced progress toward optimality and feasibility. In contrast with classical approaches, the choice of the penalty parameter ceases to be a heuristic and is determined, instead, by a subproblem with clearly defined objectives. The new penalty update strategy is presented in the context of sequential quadratic programming (SQP) and sequential linear-quadratic programming (SLQP) methods that use trust regions to promote convergence. The paper concludes with a discussion of penalty parameters for merit functions used in line search methods.

Keywords: penalty methods, sequential quadratic programming

Category 1: Nonlinear Optimization

Citation: Technical Report Optimization Technology Center Northwestern University Feb, 2006

Download: [Postscript][PDF]

Entry Submitted: 12/13/2004
Entry Accepted: 12/13/2004
Entry Last Modified: 02/25/2006

Modify/Update this entry