An Optimization algorithm for nonsmooth nonconvex problems with upper-C^2 objective

TL;DR

This paper introduces a new bundle method algorithm tailored for nonsmooth, nonconvex optimization problems with upper-C2 objectives, demonstrating improved efficiency and applicability to real-world power flow problems.

Contribution

The paper presents a simplified, more efficient bundle method for upper-C2 problems, extending its applicability to constrained nonconvex optimization with practical power system applications.

Findings

Algorithm is globally convergent with bounded parameters.

Proposed method outperforms conventional bundle methods in efficiency.

Successfully applied to real-world optimal power flow problems.

Abstract

An optimization algorithm for nonsmooth nonconvex constrained optimization problems with upper-C2 objective functions is proposed and analyzed. Upper-C2 is a weakly concave property that exists in difference of convex (DC) functions and arises naturally in many applications, particularly certain classes of solutions to parametric optimization problems [34, 4], e.g., recourse of stochastic programming [36] and projection into closed sets [34]. The algorithm can be viewed as a bundle method specialized for upper-C2 problems and is globally convergent with bounded algorithm parameters. Compared to conventional bundle methods, the proposed method is both simpler and computationally more efficient. The algorithm handles general smooth constraints similarly to sequential quadratic programming (SQP) methods and uses a line search to ensure progress. The potential inconsistencies from the linearization of the constraints are addressed through a penalty method. The capabilities of the algorithm are demonstrated by solving both simple upper-C2 problems and real-world optimal power flow problems used in current power grid industry practices.