Tightness of the maximum likelihood semidefinite relaxation for angular synchronization

Maximum likelihood estimation problems are, in general, intractable optimization problems. As a result, it is common to approximate the maximum likelihood estimator (MLE) using convex relaxations. In some cases, the relaxation is tight: it recovers the true MLE. Most tightness proofs only apply to s...

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Veröffentlicht in:	Mathematical programming Jg. 163; H. 1-2; S. 145 - 167
Hauptverfasser:	Bandeira, Afonso S., Boumal, Nicolas, Singer, Amit
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2017 Springer Nature B.V
Schlagworte:	Algorithms Analysis Approximation Calculus of Variations and Optimal Control; Optimization Combinatorics Computer science Convex analysis Estimating Estimating techniques Full Length Paper Heuristic Mathematical and Computational Physics Mathematical Methods in Physics Mathematical models Mathematical programming Mathematics Mathematics and Statistics Mathematics of Computing Maximum likelihood method Noise Numerical Analysis Optimization Phases Semidefinite programming Studies Synchronism Synchronization Theoretical Tightness Angular synchronization Maximum likelihood estimation Semidefinite programming 90C22 (Semidefinite programming) Tightness of convex relaxation 90C26 (Nonconvex programming, global optimization) 62F10 (Point estimation)
ISSN:	0025-5610, 1436-4646
Online-Zugang:	Volltext
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