The concave-convex procedure (CCCP) is a majorization-minimization algorithm that solves d.c. (difference of convex functions) programs as a sequence of convex programs. In machine learning, CCCP is extensively used in many learning algorithms like sparse support vector machines (SVMs), transductive SVMs, sparse principal component analysis, etc. Though widely used in many applications, the convergence behavior of CCCP has not gotten a lot of specific attention. In this paper, we provide a rigorous analysis of the convergence of CCCP by addressing these questions:

• (i) When does CCCP find a local minimum or a stationary point of the d.c. program under consideration?
• (ii) When does the sequence generated by CCCP converge?

We also present an open problem on the issue of local convergence of CCCP.