Convergence analysis of approximate primal solutions in dual first-order methods

TL;DR

This paper extends convergence rate guarantees for primal solutions in dual first-order methods to cases where the dual gradient is only locally Lipschitz, covering broader convex optimization problems.

Contribution

It establishes primal convergence rates under local Lipschitz continuity of the dual gradient, broadening applicability beyond globally Lipschitz cases.

Findings

Primal suboptimality and infeasibility are $O(1/\sqrt{k})$ with projected gradient.

Primal suboptimality and infeasibility are $O(1/k)$ with fast gradient methods.

Error bounds relate primal solutions to dual variable errors.

Abstract

Dual first-order methods are powerful techniques for large-scale convex optimization. Although an extensive research effort has been devoted to studying their convergence properties, explicit convergence rates for the primal iterates have only been established under global Lipschitz continuity of the dual gradient. This is a rather restrictive assumption that does not hold for several important classes of problems. In this paper, we demonstrate that primal convergence rate guarantees can also be obtained when the dual gradient is only locally Lipschitz. The class of problems that we analyze admits general convex constraints including nonlinear inequality, linear equality, and set constraints. As an approximate primal solution, we take the minimizer of the Lagrangian, computed when evaluating the dual gradient. We derive error bounds for this approximate primal solution in terms of the errors of the dual variables, and establish convergence rates of the dual variables when the dual problem is solved using a projected gradient or fast gradient method. By combining these results, we show that the suboptimality and infeasibility of the approximate primal solution at iteration $k$ are no worse than $O(1/\sqrt{k})$ when the dual problem is solved using a projected gradient method, and $O(1/k)$ when a fast dual gradient method is used.