Dynamic Contention Resolution Schemes

2026-09-18

AI Summary

本文提出动态争用解决框架（DCRSs），用于解决全动态环境下的打包问题，通过低调整策略实现对子模目标函数的优化。

Abstract

We introduce a low-recourse rounding paradigm for packing problems in fully dynamic settings, which we name Dynamic Contention Resolution Schemes (DCRSs). These are dynamic analogs of (Online) Contention Resolution Schemes (or (O)CRSs) for low-recourse dynamic optimization and offer a variety of benefits. Similarly to their offline and online counterparts, DCRSs for different constraints can be combined to obtain DCRSs for the constraints' intersection. Furthermore, together with the Positive Body Chasing framework of Bhattacharya, Buchbinder, Levin, and Saranurak [FOCS 2023], DCRSs imply competitive recourse algorithms for fully dynamic packing problems with submodular objectives: these are algorithms that, for any input sequence, incur recourse that is itself competitive with the best possible recourse for that sequence. We show the existence of $Ω(1)$-balanced and $O(\log \mathrm{rank})$-recourse DCRSs for matroid constraints, and $Ω(1)$-balanced/$O(1)$-recourse DCRSs for matching and knapsack constraints. In particular, these yield the first non-trivial recourse bound for fully dynamic knapsack, as well as the first competitive-recourse algorithm for non-bipartite matching, and both of these apply even to monotone submodular objectives. Beyond our particular results, we view the DCRS framework as a principled step towards mechanizing the relax-and-round paradigm of approximation algorithms in the context of dynamic optimization.

Problem

• Dynamic Contention Resolution Schemes
• fully dynamic settings
• packing problems
• submodular objectives
• low-recourse

Innovation

• Dynamic Contention Resolution Schemes
• low-recourse rounding
• fully dynamic settings
• submodular objectives
• competitive recourse