English

A Reduction for Optimizing Lattice Submodular Functions with Diminishing Returns

Data Structures and Algorithms 2018-05-29 v2 Artificial Intelligence Machine Learning

Abstract

A function f:Z+ER+f: \mathbb{Z}_+^E \rightarrow \mathbb{R}_+ is DR-submodular if it satisfies f(x+χi)f(x)f(y+χi)f(y)f({\bf x} + \chi_i) -f ({\bf x}) \ge f({\bf y} + \chi_i) - f({\bf y}) for all xy,iE{\bf x}\le {\bf y}, i\in E. Recently, the problem of maximizing a DR-submodular function f:Z+ER+f: \mathbb{Z}_+^E \rightarrow \mathbb{R}_+ subject to a budget constraint x1B\|{\bf x}\|_1 \leq B as well as additional constraints has received significant attention \cite{SKIK14,SY15,MYK15,SY16}. In this note, we give a generic reduction from the DR-submodular setting to the submodular setting. The running time of the reduction and the size of the resulting submodular instance depends only \emph{logarithmically} on BB. Using this reduction, one can translate the results for unconstrained and constrained submodular maximization to the DR-submodular setting for many types of constraints in a unified manner.

Keywords

Cite

@article{arxiv.1606.08362,
  title  = {A Reduction for Optimizing Lattice Submodular Functions with Diminishing Returns},
  author = {Alina Ene and Huy L. Nguyen},
  journal= {arXiv preprint arXiv:1606.08362},
  year   = {2018}
}
R2 v1 2026-06-22T14:35:25.014Z