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

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 regular...

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Vydáno v:	Computational optimization and applications Ročník 72; číslo 3; s. 727 - 768
Hlavní autoři:	Laiu, M. Paul, Tits, André L.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.04.2019 Springer Nature B.V Springer
Témata:	Active constraints identification Algorithms Constraint reduction Convex and Discrete Geometry Convex quadratic programming Iterative methods Management Science Mathematics MATHEMATICS AND COMPUTING Mathematics and Statistics Mehrotra’s predictor-corrector Operations Research Operations Research/Decision Theory Optimization Predictor-corrector methods Primal-dual interior-point method Quadratic programming Regularization Statistics Convex quadratic programming Constraint reduction Primal-dual interior-point method Mehrotra’s predictor-corrector Regularization Active constraints identification
ISSN:	0926-6003, 1573-2894
On-line přístup:	Získat plný text
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