Maximizing Symmetric Submodular Functions

TL;DR

This paper develops improved approximation algorithms for maximizing symmetric submodular functions under various constraints, outperforming previous results and providing new theoretical guarantees for these optimization problems.

Contribution

It introduces novel approximation algorithms for symmetric submodular maximization problems, achieving better ratios than existing methods and extending applicability to non-symmetric cases.

Findings

Achieves a 0.432-approximation for maximizing symmetric submodular functions over down-monotone polytopes.

Provides a constant-factor approximation for the cardinality-constrained maximization problem, always at least 0.432.

Develops a deterministic linear-time 1/2-approximation for unconstrained symmetric submodular maximization.

Abstract

Symmetric submodular functions are an important family of submodular functions capturing many interesting cases including cut functions of graphs and hypergraphs. Maximization of such functions subject to various constraints receives little attention by current research, unlike similar minimization problems which have been widely studied. In this work, we identify a few submodular maximization problems for which one can get a better approximation for symmetric objectives than the state of the art approximation for general submodular functions. We first consider the problem of maximizing a non-negative symmetric submodular function $f\colon 2^\mathcal{N} \to \mathbb{R}^+$ subject to a down-monotone solvable polytope $\mathcal{P} \subseteq [0, 1]^\mathcal{N}$. For this problem we describe an algorithm producing a fractional solution of value at least $0.432 \cdot f(OPT)$, where $OPT$ is the optimal integral solution. Our second result considers the problem $\max \{f(S) : |S| = k\}$ for a non-negative symmetric submodular function $f\colon 2^\mathcal{N} \to \mathbb{R}^+$. For this problem, we give an approximation ratio that depends on the value $k / |\mathcal{N}|$ and is always at least $0.432$. Our method can also be applied to non-negative non-symmetric submodular functions, in which case it produces $1/e - o(1)$ approximation, improving over the best known result for this problem. For unconstrained maximization of a non-negative symmetric submodular function we describe a deterministic linear-time $1/2$-approximation algorithm. Finally, we give a $[1 - (1 - 1/k)^{k - 1}]$-approximation algorithm for Submodular Welfare with $k$ players having identical non-negative submodular utility functions, and show that this is the best possible approximation ratio for the problem.