A variational approach to dual methods for constrained convex optimization

We approach linearly constrained convex optimization problems through their dual reformulation. Specifically, we derive a family of accelerated dual algorithms by adopting a variational perspective in which the dual function of the problem represents the scaled potential energy of a synthetic mechanical system, and the kinetic energy is defined by the Bregman divergence induced by the dual velocity flow. Through application of Hamilton's principle, we derive a continuous-time dynamical system which exponentially converges to the saddle point of the Lagrangian. Moreover, this dynamical system only admits a stable discretization through accelerated higher-order gradient methods, which precisely corresponds to accelerated dual mirror ascent. In particular, we obtain discrete-time convergence rate O(1/kp), where p − 1 is the truncation index of the Taylor expansion of the dual function. For practicality sake, we consider p = 2 and p = 3 only, respectively corresponding to dual Nesterov acceleration and a dual variant of Nesterov's cubic regularized Newton method. This analysis provides an explanation from whence dual acceleration comes as the discretization of the Euler-Lagrange dynamics associated with the constrained convex program. We demonstrate the performance of the aforementioned continuous-time framework with numerical simulations.