Optimal input design via Frank-Wolfe
TL;DR
The Frank--Wolfe framework is extended to input design for system identification with unknown dynamics and adaptive online LQR, and an $\mathcal{O}(1/M)$ convergence rate is established for the objective value, while strong convexity yields an $\mathcal{O}(1/\sqrt{M})$ rate for the iterates.
Abstract
We study optimal input design over a finite horizon for linear dynamical systems. The goal is to minimize a weighted inverse-covariance (information) criterion subject to an energy budget. The set of covariances achievable by causal policies is convex but lacks a tractable explicit description, ruling out projection-based methods. We show that Frank--Wolfe applies naturally: each linear minimization subproblem is a budget-constrained finite-horizon linear quadratic (LQ) problem, solvable by a Riccati recursion and one-dimensional bisection over a Lagrange multiplier. Using smoothness of the objective over the feasible set, we establish an $\mathcal{O}(1/M)$ convergence rate for the objective value, while strong convexity yields an $\mathcal{O}(1/\sqrt{M})$ rate for the iterates. We further extend the framework to input design for system identification with unknown dynamics and adaptive online LQR, and illustrate the approach numerically.