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Computational Optimization and Applications

, Volume 72, Issue 3, pp 727–768 | Cite as

A constraint-reduced MPC algorithm for convex quadratic programming, with a modified active set identification scheme

  • M. Paul LaiuEmail author
  • André L. Tits
Article
  • 47 Downloads

Abstract

A constraint-reduced Mehrotra-predictor-corrector algorithm for convex quadratic programming is proposed. (At each iteration, such algorithms use only a subset of the inequality constraints in constructing the search direction, resulting in CPU savings.) The proposed algorithm makes use of a regularization scheme to cater to cases where the reduced constraint matrix is rank deficient. Global and local convergence properties are established under arbitrary working-set selection rules subject to satisfaction of a general condition. A modified active-set identification scheme that fulfills this condition is introduced. Numerical tests show great promise for the proposed algorithm, in particular for its active-set identification scheme. While the focus of the present paper is on dense systems, application of the main ideas to large sparse systems is briefly discussed.

Keywords

Convex quadratic programming Constraint reduction Primal-dual interior-point method Mehrotra’s predictor-corrector Regularization Active constraints identification 

Notes

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Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2019

Authors and Affiliations

  1. 1.Computational and Applied Mathematics Group, Computer Science and Mathematics DivisionOak Ridge National LaboratoryOak RidgeUSA
  2. 2.Department of Electrical and Computer Engineering & Institute for Systems ResearchUniversity of MarylandCollege ParkUSA

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