Information Projection on Banach spaces with Applications to State Independent KL-Weighted Optimal Control

TL;DR

This paper investigates constrained information projections on Banach spaces with Gaussian measures, characterizing the problem through variational principles and stochastic processes, with applications to optimal control.

Contribution

It provides a comprehensive characterization of KL-divergence based projections onto shift measures in Banach spaces, including reformulations as variational problems and stochastic process representations.

Findings

Characterization of projections via a portmanteau theorem.

Equivalence to minimization of an Onsager-Machlup function.

Reformulation as a calculus of variations problem.

Abstract

This paper studies constrained information projections on Banach spaces with respect to a Gaussian reference measure. Specifically our interest lies in characterizing projections of the reference measure, with respect to the KL-divergence, onto sets of measures corresponding to changes in the mean (or {\it shift measures}). As our main result, we give a portmanteau theorem that characterizes the relationship among several different formulations of this problem. In the general setting of Gaussian measures on a Banach space, we show that this information projection problem is equivalent to minimization of a certain Onsager-Machlup (OM) function with respect to an associated stochastic process. We then construct several reformulations in the more specific setting of classical Wiener space. First, we show that KL-weighted optimization over shift measures can also be expressed in terms of an OM function for an associated stochastic process that we are able to characterize. Next, we show how to encode the feasible set of shift measures through an explicit functional constraint by constructing an appropriate penalty function. Finally, we express our information projection problem as a calculus of variations problem, which suggests a solution procedure via the Euler-Lagrange equation. We work out the details of these reformulations for several specific examples.