Online Submodular Maximization with Preemption

TL;DR

This paper investigates an online submodular maximization problem where elements arrive sequentially, and the algorithm can accept or preempt elements to maximize the submodular function under different constraints.

Contribution

It introduces a novel online model with preemption for submodular maximization and provides competitive algorithms with proven bounds for unconstrained and cardinality-constrained cases.

Findings

Achieved a 1/e-competitive algorithm for unconstrained case.

Developed constant competitive ratio algorithms for cardinality-constrained case.

Established upper and lower bounds on the competitive ratio for the problem.

Abstract

Submodular function maximization has been studied extensively in recent years under various constraints and models. The problem plays a major role in various disciplines. We study a natural online variant of this problem in which elements arrive one-by-one and the algorithm has to maintain a solution obeying certain constraints at all times. Upon arrival of an element, the algorithm has to decide whether to accept the element into its solution and may preempt previously chosen elements. The goal is to maximize a submodular function over the set of elements in the solution. We study two special cases of this general problem and derive upper and lower bounds on the competitive ratio. Specifically, we design a $1/e$-competitive algorithm for the unconstrained case in which the algorithm may hold any subset of the elements, and constant competitive ratio algorithms for the case where the algorithm may hold at most $k$ elements in its solution.