Turnpike in optimal control of PDEs, ResNets, and beyond

TL;DR

This paper explores the turnpike property in optimal control problems, its mathematical foundations, and applications to PDEs, economics, and deep learning, highlighting its role in simplifying long-term control strategies.

Contribution

It reviews recent developments in turnpike theory, introduces novel applications in neural networks, and connects economic insights with PDE control and deep learning.

Findings

Turnpike property holds for various PDEs in mechanics.

Optimal controls are nearly constant over most of the time horizon.

Applications include stability estimates in deep learning and Hamilton-Jacobi-Bellman asymptotics.

Abstract

The \emph{turnpike property} in contemporary macroeconomics asserts that if an economic planner seeks to move an economy from one level of capital to another, then the most efficient path, as long as the planner has enough time, is to rapidly move stock to a level close to the optimal stationary or constant path, then allow for capital to develop along that path until the desired term is nearly reached, at which point the stock ought to be moved to the final target. Motivated in part by its nature as a resource allocation strategy, over the past decade, the turnpike property has also been shown to hold for several classes of partial differential equations arising in mechanics. When formalized mathematically, the turnpike theory corroborates the insights from economics: for an optimal control problem set in a finite-time horizon, optimal controls and corresponding states, are close (often exponentially), during most of the time, except near the initial and final time, to the optimal control and corresponding state for the associated stationary optimal control problem. In particular, the former are mostly constant over time. This fact provides a rigorous meaning to the asymptotic simplification that some optimal control problems appear to enjoy over long time intervals, allowing the consideration of the corresponding stationary problem for computing and applications. We review a slice of the theory developed over the past decade --the controllability of the underlying system is an important ingredient, and can even be used to devise simple turnpike-like strategies which are nearly optimal--, and present several novel applications, including, among many others, the characterization of Hamilton-Jacobi-Bellman asymptotics, and stability estimates in deep learning via residual neural networks.