On the Arrow-Hurwicz differential system for linearly constrained convex minimization

TL;DR

This paper analyzes the convergence properties of the Arrow-Hurwicz differential system in solving linearly constrained convex minimization problems, providing theoretical insights and numerical validation.

Contribution

It extends classical analysis by establishing convergence conditions, decay estimates, and connections to second-order systems for the Arrow-Hurwicz method in convex optimization.

Findings

Solutions converge to saddle points under certain conditions.

Asymptotic decay rates are established for solutions and primal-dual gaps.

Results apply to structured convex minimization problems with numerical support.

Abstract

In a real Hilbert space setting, we reconsider the classical Arrow-Hurwicz differential system in view of solving linearly constrained convex minimization problems. We investigate the asymptotic properties of the differential system and provide conditions for which its solutions converge towards a saddle point of the Lagrangian associated with the convex minimization problem. Our convergence analysis mainly relies on a `Lagrangian identity' which naturally extends on the well-known descent property of the classical continuous steepest descent method. In addition, we present asymptotic estimates on the decay of the solutions and the primal-dual gap function measured in terms of the Lagrangian. These estimates are further refined to the ones of the classical damped harmonic oscillator provided that second-order information on the objective function of the convex minimization problem is available. Finally, we show that our results directly translate to the case of solving structured convex minimization problems. Numerical experiments further illustrate our theoretical findings.