Duality Gap Estimation via a Refined Shapley--Folkman Lemma

Based on concepts like the kth convex hull and finer characterization of nonconvexity of a function, we propose a refinement of the Shapley-Folkman lemma and derive a new estimate for the duality gap of nonconvex optimization problems with separable objective functions. We apply our result to the network utility maximization problem in networking and the dynamic spectrum management problem in communication as examples to demonstrate that the new bound can be qualitatively tighter than the existing ones. The idea is also applicable to cases with general nonconvex constraints.