On the Complexity of an Augmented Lagrangian Method for Nonconvex Optimization

Understand

In this paper we study the worst-case complexity of an inexact Augmented Lagrangian method for nonconvex constrained problems.

• Assuming that the penalty parameters are bounded, we prove a complexity bound of $\mathcal{O}(|\log(\epsilon)|)$ outer iterations for the referred algorithm to generate an $\epsilon$-approximate KKT point, for $\epsilon\in (0,1)$.
• When the penalty parameters are unbounded, we prove an outer iteration complexity bound of $\mathcal{O}\left(\epsilon^{-2/(\alpha-1)}\right)$, where $\alpha>1$ controls the rate of increase of the penalty parameters.
• For linearly constrained problems, these bounds yield to evaluation complexity bounds of $\mathcal{O}(|\log(\epsilon)|^{2}\epsilon^{-2})$ and $\mathcal{O}\left(\epsilon^{-\left(\frac{2(2+\alpha)}{\alpha-1}+2\right)}\right)$, respectively, when appropriate first-order methods ($p=1$) are used to approximately solve the unconstrained subproblems at each iteration.