Asymptotically tight Lagrangian dual of smooth nonconvex problems via improved error bound of Shapley-Folkman Lemma

TL;DR

This paper refines the geometric understanding of the Shapley-Folkman Lemma, showing that under certain smoothness conditions, the Minkowski sum of nonconvex sets becomes approximately convex, leading to tighter duality bounds in nonconvex optimization.

Contribution

It provides an elementary geometric proof of the lemma, refines the error bounds, and demonstrates conditions under which the sum converges to convexity, improving duality gap estimates.

Findings

Refined error bounds for the Shapley-Folkman Lemma.

Conditions under which Minkowski sums of nonconvex sets become approximately convex.

Implication of asymptotically tight Lagrangian duals for smooth nonconvex problems.

Abstract

In convex geometry, the Shapley-Folkman Lemma asserts that the nonconvexity of a Minkowski sum of $n$ dimensional bounded nonconvex sets does not accumulate once the number of summands exceeds the dimension $n$, and thus the sum becomes approximately convex. Originally published by Starr in the context of quasi-equilibrium in nonconvex market models in economics, the lemma has since found widespread use in optimization, particularly for estimating the duality gap of the Lagrangian dual of separable nonconvex problems. Given its foundational nature, we pose the following geometric question: Is it possible for the nonconvexity of the Minkowski sum of $n$-dimensional nonconvex sets to even diminish instead of just not accumulating as the number of summands increases, under some general conditions? We answer this affirmatively. First, we provide an elementary geometric proof of the Shapley-Folkman Lemma based on the facial structure of the convex hull of each set. This leads to refinement of the classical error bound derived from the lemma. Building on this new geometric perspective, we further show that when most of the sets satisfy a certain local smoothness condition which naturally arises in the epigraphs of smooth functions, their Minkowski sum converges directly to a convex set, with a vanishing nonconvexity measure. In optimization, this implies that the Lagrangian dual of block-structured smooth nonconvex problems with potentially additional sparsity constraints is asymptotically tight under mild assumptions, which contracts nonvanishing duality gap obtained via classical Shapley-Folkman Lemma.