CVX

A smoothed augmented Lagrangian framework for convex optimization with nonsmooth constraints
Please click on the PDF to view the preprint version of this publication.
Peixuan Zhang , Uday V. Shanbhag, Ethan X. Fang
PDF
A smoothed augmented Lagrangian framework for nonsmooth convex optimization
We focus on developing an Augmented Lagrangian Method (ALM) frame- work for resolving nonsmooth convex optimization problems. The problem of interest is formulated as follows. $\min_{\mathbf{x}\in\mathcal{X}} f(\mathbf{x}) \quad \text{subject to} \quad g(\mathbf{x}) \leq 0$
Preprint paper
A smoothed augmented Lagrangian framework for nonsmooth convex optimization