Low-Complexity Modeling of Partially Available Second-Order Statistics: Theory and an Efficient Matrix Completion Algorithm

State statistics of linear systems satisfy certain structural constraints that arise from the underlying dynamics and the directionality of input disturbances. In the present paper, we study the problem of completing partially known state statistics. Our aim is to develop tools that can be used in t...

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Vydané v:	IEEE transactions on automatic control Ročník 62; číslo 3; s. 1368 - 1383
Hlavní autori:	Zare, Armin, Yongxin Chen, Jovanovic, Mihailo R., Georgiou, Tryphon T.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.03.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Alternating minimization algorithm convex optimization Covariance matrices Customization disturbance dynamics Excitation Heuristic algorithms Inverse problems Linear systems low-rank approximation Mathematical model Mathematical problems matrix completion problems Matrix methods nuclear norm regularization Optimization Solvers Statistics Steady-state Stochastic processes structured covariances
ISSN:	0018-9286, 1558-2523
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