Complexity of proximal augmented Lagrangian for nonconvex optimization with nonlinear equality constraints

Abstract

We analyze worst-case complexity of a Proximal augmented Lagrangian (Proximal AL) framework for nonconvex optimization with nonlinear equality constraints. When an approximate first-order (second-order) optimal point is obtained in the subproblem, an ϵ first-order (second-order) optimal point for the original problem can be guaranteed within $\mathcal{O}(1/\epsilon^{2−\eta})$ outer iterations (where $\eta$ is a user-defined parameter with $\eta \in [0,2]$ for the first-order result and $\eta \in [1,2]$ for the second-order result) when the proximal term coefficient $\beta$ and penalty parameter $\rho$ satisfy $\beta = \mathcal{O}(\epsilon^\eta)$ and $\rho = \Omega(1/\epsilon^\eta)$, respectively. We also investigate the total iteration complexity and operation complexity when a Newton-conjugate-gradient algorithm is used to solve the subproblems. Finally, we discuss an adaptive scheme for determining a value of the parameter ρ that satisfies the requirements of the analysis.

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