Approximation Algorithms for Submodular Multiway Partition

We study algorithms for the SUBMODULAR Multiway PARTITION problem (SUB-MP). An instance of SUB-MP consists of a finite ground set V, a subset S = {s 1, S 2, .. ., s k } ⊆ V of k elements called terminals, and a non-negative submodular set function f: 2 V → ℝ + on V provided as a value oracle. The goal is to partition V into k sets A 1, .. ., A k to minimize Σ i=1 k f(A i ) such that for 1 ≤ i ≤ k, s i ∈ A i. SUB-MP generalizes some well-known problems such as the MULTIWAY CUT problem in graphs and hypergraphs, and the NODE-WEIGHED MULTIWAY Cut problem in graphs. SUB-MP for arbitrary sub- modular functions (instead of just symmetric functions) was considered by Zhao, Nagamochi and Ibaraki [29]. Previous algorithms were based on greedy splitting and divide and conquer strategies. In recent work [5] we proposed a convex-programming relaxation for SUB-MP based on the Lovasz-extension of a submodular function and showed its applicability for some special cases. In this paper we obtain the following results for arbitrary submodular functions via this relaxation. (1) A 2-approximation for SUB-MP. This improves the (k - 1)-approximation from [29]. (2) A (1. 5 - 1/k)-approximation for SUB-MP when f is symmetric. This improves the 2(1 - 1/k)-approximation from [23], [29].

Authors

• Chandra Chekuri
• Alina Ene

Keywords

• Approximation methods
• Resource management
• Approximation algorithms
• Partitioning algorithms
• Polynomials
• Vectors
• Algorithm design and analysis
• Divide-and-conquer
• Nonnegative Function
• Symmetric Function
• Hypergraph
• Partitioning Problem
• Graph Problems
• Cylindrical
• Objective Function
• Random Permutations
• Proof Of The Lemma
• Constant Approximation
• Convex Relaxation
• Linear Programming Relaxation
• Key Lemma