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A Sequential Quadratic Programming Algorithm with an Additional Equality Constrained Phase

Jose Luis Morales(jmorales***at***itam.mx)
Jorge Nocedal(nocedal***at***eecs.northwestern.edu)
Yuchen Wu(rainson.wood***at***gmail.com)

Abstract: A sequential quadratic programming (SQP) method is presented that aims to overcome some of the drawbacks of contemporary SQP methods. It avoids the difficulties associated with indefinite quadratic programming subproblems by defining this subproblem to be always convex. The novel feature of the approach is the addition of an equality constrained phase that promotes fast convergence and improves performance in the presence of ill conditioning. This equality constrained phase uses exact second order information and can be implemented using either a direct solve or an iterative method. The paper studies the global and local convergence properties of the new algorithm and presents a set of numerical experiments to illustrate its practical performance.

Keywords: constrained optimization, sequential quadratic programming

Category 1: Nonlinear Optimization

Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: Report 05/2008, Optimization Center, Northwestern University

Download: [PDF]

Entry Submitted: 12/29/2008
Entry Accepted: 01/08/2009
Entry Last Modified: 12/29/2008

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