Submodular Cost Allocation Problem and Applications

TL;DR

This paper introduces a unified convex relaxation approach for the Minimum Submodular-Cost Allocation problem, leading to new approximation algorithms for various combinatorial problems like multiway cut and hypergraph partitioning.

Contribution

It develops a convex-programming relaxation via Lovász extension, unifies previous methods, and provides improved approximation algorithms for several complex partitioning problems.

Findings

Achieves a (1.5 - 1/k)-approximation for hypergraph multiway partition.

Provides a min{2(1-1/k), H_Δ}-approximation for hypergraph multiway cut.

Unifies and extends previous relaxations and rounding techniques.

Abstract

We study the Minimum Submodular-Cost Allocation problem (MSCA). In this problem we are given a finite ground set $V$ and $k$ non-negative submodular set functions $f_1 ,..., f_k$ on $V$. The objective is to partition $V$ into $k$ (possibly empty) sets $A_1 ,..., A_k$ such that the sum $\sum_{i=1}^k f_i(A_i)$ is minimized. Several well-studied problems such as the non-metric facility location problem, multiway-cut in graphs and hypergraphs, and uniform metric labeling and its generalizations can be shown to be special cases of MSCA. In this paper we consider a convex-programming relaxation obtained via the Lov\'asz-extension for submodular functions. This allows us to understand several previous relaxations and rounding procedures in a unified fashion and also develop new formulations and approximation algorithms for several problems. In particular, we give a $(1.5 - 1/k)$-approximation for the hypergraph multiway partition problem. We also give a $\min\{2(1-1/k), H_{\Delta}\}$-approximation for the hypergraph multiway cut problem when $\Delta$ is the maximum hyperedge size. Both problems generalize the multiway cut problem in graphs and the hypergraph cut problem is approximation equivalent to the node-weighted multiway cut problem in graphs.