# An Approximate Shapley-Folkman Theorem

arXiv: https://arxiv.org/abs/1712.08559 · HTML: https://mlsanity.com/paper/1712.08559/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2017.

## Built on by (most cited first, 2 of 2)

- [Local and Global Uniform Convexity Conditions](https://mlsanity.com/paper/2102.05134/) (2021)
- [The Nonconvex Geometry of Linear Inverse Problems](https://mlsanity.com/paper/2101.02776/) (2021)

## Abstract (arXiv)

The Shapley-Folkman theorem shows that Minkowski averages of uniformly bounded sets tend to be convex when the number of terms in the sum becomes much larger than the ambient dimension. In optimization, Aubin and Ekeland [1976] show that this produces an a priori bound on the duality gap of separable nonconvex optimization problems involving finite sums. This bound is highly conservative and depends on unstable quantities, and we relax it in several directions to show that non convexity can have a much milder impact on finite sum minimization problems such as empirical risk minimization and multi-task classification. As a byproduct, we show a new version of Maurey's classical approximate Carath'eodory lemma where we sample a significant fraction of the coefficients, without replacement, as well as a result on sampling constraints using an approximate Helly theorem, both of independent interest.