A sequential cutting plane algorithm for solving convex NLP problems

In this paper we look at a new algorithm for solving convex nonlinear programming optimization problems. The algorithm is a cutting plane-based method, where the sizes of the subproblems remain fixed, thus avoiding the issue with constantly growing subproblems we have for the classical Kelley's cutting plane algorithm. Initial numerical experiments indicate that the algorithm is considerably faster than Kelley's cutting plane algorithm and also competitive with existing nonlinear programming algorithms.