Disciplined Convex-Concave Programming

X. Shen, S. Diamond, Y. Gu, and S. Boyd

Working draft, April 2016. Shorter version appeared in Proceedings IEEE Conference on Decision and Control, pages 1009-1014, December 2016.

In this paper we introduce disciplined convex-concave programming (DCCP), which combines the ideas of disciplined convex programming (DCP) with convex-concave programming (CCP). Convex-concave programming is an organized heuristic for solving nonconvex problems that involve objective and constraint functions that are a sum of a convex and a concave term. DCP is a structured way to define convex optimization problems, based on a family of basic convex and concave functions and a few rules for combining them. Problems expressed using DCP can be automatically converted to standard form and solved by a generic solver; widely used implementations include YALMIP, CVX, CVXPY, and Convex.jl. In this paper we propose a framework that combines the two ideas, and includes two improvements over previously published work on convex-concave programming, specifically the handling of domains of the functions, and the issue of nondifferentiability on the boundary of the domains. We describe a Python implementation called DCCP, which extends CVXPY, and give examples.