A General Framework for Learning-Augmented Online Allocation

Ilan Reuven Cohen, Debmalya Panigrahi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Online allocation is a broad class of problems where items arriving online have to be allocated to agents who have a fixed utility/cost for each assigned item so to maximize/minimize some objective. This framework captures a broad range of fundamental problems such as the Santa Claus problem (maximizing minimum utility), Nash welfare maximization (maximizing geometric mean of utilities), makespan minimization (minimizing maximum cost), minimization of ℓp-norms, and so on. We focus on divisible items (i.e., fractional allocations) in this paper. Even for divisible items, these problems are characterized by strong super-constant lower bounds in the classical worst-case online model. In this paper, we study online allocations in the learning-augmented setting, i.e., where the algorithm has access to some additional (machine-learned) information about the problem instance. We introduce a general algorithmic framework for learning-augmented online allocation that produces nearly optimal solutions for this broad range of maximization and minimization objectives using only a single learned parameter for every agent. As corollaries of our general framework, we improve prior results of Lattanzi et al. (SODA 2020) and Li and Xian (ICML 2021) for learning-augmented makespan minimization, and obtain the first learning-augmented nearly-optimal algorithms for the other objectives such as Santa Claus, Nash welfare, ℓp-minimization, etc. We also give tight bounds on the resilience of our algorithms to errors in the learned parameters, and study the learnability of these parameters.

Original languageEnglish
Title of host publication50th International Colloquium on Automata, Languages, and Programming, ICALP 2023
EditorsKousha Etessami, Uriel Feige, Gabriele Puppis
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959772785
DOIs
StatePublished - Jul 2023
Event50th International Colloquium on Automata, Languages, and Programming, ICALP 2023 - Paderborn, Germany
Duration: 10 Jul 202314 Jul 2023

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume261
ISSN (Print)1868-8969

Conference

Conference50th International Colloquium on Automata, Languages, and Programming, ICALP 2023
Country/TerritoryGermany
CityPaderborn
Period10/07/2314/07/23

Bibliographical note

Publisher Copyright:
© Ilan Reuven Cohen and Debmalya Panigrahi.

Funding

Funding Ilan Reuven Cohen: This research was supported by the Israel Science Foundation grant No. 1737/21. Debmalya Panigrahi: This research was supported in part by NSF grants CCF-1750140 (CAREER) and CCF-1955703.

FundersFunder number
National Science FoundationCCF-1955703, CCF-1750140
Israel Science Foundation1737/21

    Keywords

    • Algorithms with predictions
    • Online algorithms
    • Scheduling algorithms

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