Paper Info
Title
A Constraint-Reduced MPC Algorithm for Convex Quadratic Programming, with a Modified Active Set Identification Scheme
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.
Year
DOI
Venue
2019
10.1007/s10589-019-00058-0
Computational Optimization and Applications
Keywords
Field
DocType
Convex quadratic programming, Constraint reduction, Primal-dual interior-point method, Mehrotra’s predictor-corrector, Regularization, Active constraints identification
Numerical tests,Mathematical optimization,Identification scheme,Constraint reduction,Algorithm,Convex quadratic programming,Regularization (mathematics),Local convergence,Constraint matrix,Mathematics
Journal
Volume
Issue
ISSN
72.0
3
1573-2894
Citations 
PageRank 
References 
0
0.34
17
Authors
2
Name
Order
Citations
PageRank
M. Paul Laiu111.40
André L. Tits242976.27