English

Maximizing k-Submodular Functions and Beyond

Data Structures and Algorithms 2016-08-05 v2 Discrete Mathematics

Abstract

We consider the maximization problem in the value oracle model of functions defined on kk-tuples of sets that are submodular in every orthant and rr-wise monotone, where k2k\geq 2 and 1rk1\leq r\leq k. We give an analysis of a deterministic greedy algorithm that shows that any such function can be approximated to a factor of 1/(1+r)1/(1+r). For r=kr=k, we give an analysis of a randomised greedy algorithm that shows that any such function can be approximated to a factor of 1/(1+k/2)1/(1+\sqrt{k/2}). In the case of k=r=2k=r=2, the considered functions correspond precisely to bisubmodular functions, in which case we obtain an approximation guarantee of 1/21/2. We show that, as in the case of submodular functions, this result is the best possible in both the value query model, and under the assumption that NPRPNP\neq RP. Extending a result of Ando et al., we show that for any k3k\geq 3 submodularity in every orthant and pairwise monotonicity (i.e. r=2r=2) precisely characterize kk-submodular functions. Consequently, we obtain an approximation guarantee of 1/31/3 (and thus independent of kk) for the maximization problem of kk-submodular functions.

Keywords

Cite

@article{arxiv.1409.1399,
  title  = {Maximizing k-Submodular Functions and Beyond},
  author = {Justin Ward and Stanislav Zivny},
  journal= {arXiv preprint arXiv:1409.1399},
  year   = {2016}
}

Comments

Full version of a SODA'14 paper, to appear in ACM Transactions on Algorithms (TALG)

R2 v1 2026-06-22T05:48:28.498Z