Feasible Sequential Quadratic Programming for Finely Discretized Problems from SIP

  • Craig T. Lawrence
  • André L. Tits
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 25)

Abstract

A Sequential Quadratic Programming algorithm designed to efficiently solve nonlinear optimization problems with many inequality constraints, e.g. problems arising from finely discretized Semi-Infinite Programming, is described and analyzed. The key features of the algorithm are (i) that only a few of the constraints are used in the QP sub-problems at each iteration , and (ii) that every iterate satisfies all constraints.

Keywords

Search Direction Line Search Accumulation Point Sequential Quadratic Programming Local Convergence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Craig T. Lawrence
    • 1
  • André L. Tits
    • 1
  1. 1.Department of Electrical Engineering and Institute for Systems ResearchUniversity of Maryland, College ParkCollege ParkUSA

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