Approximation algorithms for homogeneous polynomial optimization with quadratic constraints

In this paper, we consider approximation algorithms for optimizing a generic multi-variate homogeneous polynomial function, subject to homogeneous quadratic constraints. Such optimization models have wide applications, e.g., in signal processing, magnetic resonance imaging (MRI), data training, appr...

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Veröffentlicht in:	Mathematical programming Jg. 125; H. 2; S. 353 - 383
Hauptverfasser:	He, Simai, Li, Zhening, Zhang, Shuzhong
Format:	Journal Article Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer-Verlag 01.10.2010 Springer Springer Nature B.V
Schlagworte:	Algorithms Applied sciences Approximation Calculus of Variations and Optimal Control; Optimization Combinatorics Ellipsoids Exact sciences and technology Full Length Paper Intersections Linear algebra Magnetic resonance imaging Mathematical analysis Mathematical and Computational Physics Mathematical Methods in Physics Mathematical models Mathematical programming Mathematics Mathematics and Statistics Mathematics of Computing Maximization Numerical Analysis Operational research and scientific management Operational research. Management science Optimization Polynomials Quantum physics Signal processing Studies Theoretical Voice recognition Multi-linear tensor form 90C26 90C59 15A69 Approximation algorithm Polynomial function optimization Intersection Non convex programming Linear form Polynomial approximation Worst case method Constraint satisfaction Polynomial method Modeling Convex programming Sphere Portfolio selection Convex function Approximation theory Cartesian product Mathematical programming Quadratic programming Set constraint Nuclear magnetic resonance imaging Constrained optimization Polynomial time Polynomial function NP hard problem Signal processing Homogeneous function Ellipsoid Portfolio management Non convex analysis
ISSN:	0025-5610, 1436-4646
Online-Zugang:	Volltext
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