Enhanced Deterministic Approximation Algorithm for Non-monotone Submodular Maximization under Knapsack Constraint with Linear Query Complexity

TL;DR

This paper presents an improved deterministic approximation algorithm for non-monotone submodular maximization under knapsack constraints, achieving a better approximation factor while maintaining linear query complexity.

Contribution

It enhances the approximation factor from 6+ε to 5+ε for the fastest deterministic algorithm, with optimized components and tighter analysis.

Findings

Improved approximation factor to 5+ε

Maintains linear query complexity of O(n)

Optimized threshold greedy and candidate set construction

Abstract

In this work, we consider the Submodular Maximization under Knapsack (SMK) constraint problem over the ground set of size $n$. The problem recently attracted a lot of attention due to its applications in various domains of combination optimization, artificial intelligence, and machine learning. We improve the approximation factor of the fastest deterministic algorithm from $6+\epsilon$ to $5+\epsilon$ while keeping the best query complexity of $O(n)$, where $\epsilon >0$ is a constant parameter. Our technique is based on optimizing the performance of two components: the threshold greedy subroutine and the building of two disjoint sets as candidate solutions. Besides, by carefully analyzing the cost of candidate solutions, we obtain a tighter approximation factor.