Low-complexity modeling of partially available second-order statistics: theory and an efficient matrix completion algorithm

TL;DR

This paper introduces a convex optimization framework for completing partially known second-order statistics of large-scale linear systems, enabling control-oriented modeling with efficient algorithms and theoretical convergence guarantees.

Contribution

It develops a novel rank minimization approach using nuclear norm relaxation, formulates it as a semidefinite program, and proposes a scalable alternating minimization algorithm with proven convergence.

Findings

The proposed method effectively completes partial second-order statistics.

The AMA algorithm converges sub-linearly with fixed step-size.

Application demonstrates utility in control-oriented modeling.

Abstract

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 the context of control-oriented modeling of large-scale dynamical systems. For the type of applications we have in mind, the dynamical interaction between state variables is known while the directionality and dynamics of input excitation is often uncertain. Thus, the goal of the mathematical problem that we formulate is to identify the dynamics and directionality of input excitation in order to explain and complete observed sample statistics. More specifically, we seek to explain correlation data with the least number of possible input disturbance channels. We formulate this inverse problem as rank minimization, and for its solution, we employ a convex relaxation based on the nuclear norm. The resulting optimization problem is cast as a semidefinite program and can be solved using general-purpose solvers. For problem sizes that these solvers cannot handle, we develop a customized alternating minimization algorithm (AMA). We interpret AMA as a proximal gradient for the dual problem and prove sub-linear convergence for the algorithm with fixed step-size. We conclude with an example that illustrates the utility of our modeling and optimization framework and draw contrast between AMA and the commonly used alternating direction method of multipliers (ADMM) algorithm.