A dual gradient-projection method for large-scale strictly convex quadratic problems

The details of a solver for minimizing a strictly convex quadratic objective function subject to general linear constraints are presented. The method uses a gradient projection algorithm enhanced with subspace acceleration to solve the bound-constrained dual optimization problem. Such gradient proje...

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Veröffentlicht in:	Computational optimization and applications Jg. 67; H. 1; S. 1 - 38
Hauptverfasser:	Gould, Nicholas I. M., Robinson, Daniel P.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.05.2017 Springer Nature B.V
Schlagworte:	Acceleration Algorithms Computation Computer science Convex and Discrete Geometry Lagrange multiplier Linear algebra Management Science Mathematical analysis Mathematical models Mathematics Mathematics and Statistics Methods Operations Research Operations Research/Decision Theory Optimization Projection Quadratic programming Software packages Statistics Studies Gradient projection Large-scale optimization Sparse factorizations Quadratic programming Convex optimization Dual method
ISSN:	0926-6003, 1573-2894
Online-Zugang:	Volltext
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