Diverse Approximations for Monotone Submodular Maximization Problems with a Matroid Constraint

Autor:	Do, Anh Viet, Guo, Mingyu, Neumann, Aneta, Neumann, Frank
Rok vydání:	2023
Předmět:	Computer Science - Data Structures and Algorithms
Druh dokumentu:	Working Paper
Popis:	Finding diverse solutions to optimization problems has been of practical interest for several decades, and recently enjoyed increasing attention in research. While submodular optimization has been rigorously studied in many fields, its diverse solutions extension has not. In this study, we consider the most basic variants of submodular optimization, and propose two simple greedy algorithms, which are known to be effective at maximizing monotone submodular functions. These are equipped with parameters that control the trade-off between objective and diversity. Our theoretical contribution shows their approximation guarantees in both objective value and diversity, as functions of their respective parameters. Our experimental investigation with maximum vertex coverage instances demonstrates their empirical differences in terms of objective-diversity trade-offs. Comment: Preprint of a paper to be published in IJCAI'23
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.07567 Zobrazit plný text záznamu View this record from Arxiv