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A Line Search Exact Penalty Method Using Steering Rules

Richard Byrd(Richard.Byrd***at***Colorado.EDU)
Gabriel Lopez-Calva(glopezcalva***at***yahoo.com)
Jorge Nocedal(nocedal***at***eecs.northwestern.edu)

Abstract: Line search algorithms for nonlinear programming must include safeguards to enjoy global convergence properties. This paper describes an exact penalization approach that extends the class of problems that can be solved with line search SQP methods. In the new algorithm, the penalty parameter is adjusted at every iteration to ensure sufficient progress in linear feasibility and to promote acceptance of the step. A trust region is used to assist in the determination of the penalty parameter (but not in the step computation). It is shown that the algorithm enjoys favorable global convergence properties. Numerical experiments illustrate the behavior of the algorithm on various difficult situations.

Keywords: nonlinear programming, penalty method, sequential quadratic programming

Category 1: Nonlinear Optimization

Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: Technical Report OTC 01/009 Optimization Center Northwestern University

Download: [PDF]

Entry Submitted: 01/11/2009
Entry Accepted: 01/11/2009
Entry Last Modified: 01/11/2009

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