Proximal algorithms for large-scale statistical modeling and sensor/actuator selection

TL;DR

This paper introduces a unified proximal algorithm framework for large-scale statistical modeling and sensor/actuator selection in dynamical systems, enabling efficient solutions to complex semi-definite programs.

Contribution

It develops customized proximal algorithms that exploit problem structure, allowing scalable solutions for statistical modeling and sensor/actuator selection problems.

Findings

Algorithms handle larger problems than existing solvers.

Linear convergence of the proximal gradient method is established.

Examples demonstrate the framework's effectiveness and advantages.

Abstract

Several problems in modeling and control of stochastically-driven dynamical systems can be cast as regularized semi-definite 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 alternating direction method of multipliers, and provide examples that illustrate the merits and effectiveness of our framework.