Proximal Algorithms for Large-Scale Statistical Modeling and Sensor/Actuator Selection

Several problems in modeling and control of stochastically driven dynamical systems can be cast as regularized semidefinite programs. We examine two such representative problems and show that they can be formulated in a similar manner. The first, in statistical modeling, seeks to reconcile observed...

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Bibliographic Details
Published in:IEEE transactions on automatic control 2020-08, Vol.65 (8), p.3441-3456
Main Authors: Zare, Armin, Mohammadi, Hesameddin, Dhingra, Neil K., Georgiou, Tryphon T., Jovanovic, Mihailo R.
Format: Article
Language:English
Subjects:
Actuator selection
Actuators
Algorithms
Control systems
Covariance matrices
Heuristic algorithms
Mathematical model
method of multipliers (MM)
nonsmooth convex optimization
Perturbation methods
proximal algorithms
regularization for design
semidefinite programming
sensor selection
Sensors
Solvers
sparsity-promoting estimation and control
Statistical models
Stochastic processes
structured covariances
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Summary:Several problems in modeling and control of stochastically driven dynamical systems can be cast as regularized semidefinite programs. We examine two such representative problems and show that they can be formulated in a similar manner. The first, in statistical modeling, seeks to reconcile observed statistics by suitably and minimally perturbing prior dynamics. The second seeks to optimally select a subset of available sensors and actuators for control purposes. To address modeling and control of large-scale systems, we develop a unified algorithmic framework using proximal methods. Our customized algorithms exploit problem structure and allow handling statistical modeling, as well as sensor and actuator selection, for substantially larger scales than what is amenable to current general-purpose solvers. We establish linear convergence of the proximal gradient algorithm, draw contrast between the proposed proximal algorithms and the alternating direction method of multipliers, and provide examples that illustrate the merits and effectiveness of our framework.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2019.2948268