An optimal algorithm and superrelaxation for minimization of a quadratic function subject to separable convex constraints with applications

We propose a modification of our MPGP algorithm for the solution of bound constrained quadratic programming problems so that it can be used for minimization of a strictly convex quadratic function subject to separable convex constraints. Our active set based algorithm explores the faces by conjugate...

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Veröffentlicht in:	Mathematical programming Jg. 135; H. 1-2; S. 195 - 220
Hauptverfasser:	Dostál, Zdeněk, Kozubek, Tomáš
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer-Verlag 01.10.2012 Springer Springer Nature B.V
Schlagworte:	Algorithms Applied sciences Calculus of variations and optimal control Calculus of Variations and Optimal Control; Optimization Cantilever beams Combinatorics Conjugate gradients Eigenvalues Exact sciences and technology Friction Full Length Paper Innovations Mathematical analysis Mathematical and Computational Physics Mathematical Methods in Physics Mathematical models Mathematical programming Mathematics Mathematics and Statistics Mathematics of Computing Minimization Numerical Analysis Operational research and scientific management Operational research. Management science Optimization Quadratic programming Sciences and techniques of general use Theoretical 90C20 90C25 65K10 90C90 QPQC with separable constraints Rate of convergence Spherical constraints Error estimation Active set method Modeling Convex programming Interval arithmetic Cantilever beam Bounded solution Separation method Convergence rate Variational calculus Convex function Eigenvalue problem Hessian matrices Mathematical programming Conjugate gradient method Optimal algorithm Minimization Quadratic programming Friction Particular solution Equality constraint Quadratic function
ISSN:	0025-5610, 1436-4646
Online-Zugang:	Volltext
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