Near-Optimal Coalition Structures in Polynomial Time

TL;DR

This paper demonstrates that sparse relaxation methods can find near-optimal coalition structures efficiently in polynomial time, outperforming traditional dynamic programming and MILP approaches which require exponential time.

Contribution

It introduces a probabilistic analysis showing sparse relaxations achieve near-optimal solutions in polynomial time, unlike DP and MILP methods.

Findings

Sparse relaxations recover near-optimal coalition structures in polynomial time.

DP and MILP algorithms require exponential time for similar solution quality.

The study establishes a probabilistic separation favoring sparse relaxations for coalition structure generation.

Abstract

We study the classical coalition structure generation (CSG) problem and compare the anytime behavior of three algorithmic paradigms: dynamic programming (DP), MILP branch-and-bound, and sparse relaxations based on greedy or $l_1$-type methods. Under a simple random "sparse synergy" model for coalition values, we prove that sparse relaxations recover coalition structures whose welfare is arbitrarily close to optimal in polynomial time with high probability. In contrast, broad classes of DP and MILP algorithms require exponential time before attaining comparable solution quality. This establishes a rigorous probabilistic anytime separation in favor of sparse relaxations, even though exact methods remain ultimately optimal.