License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ICALP.2023.43
URN: urn:nbn:de:0030-drops-180952
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18095/
Cohen, Ilan Reuven ;
Panigrahi, Debmalya
A General Framework for Learning-Augmented Online Allocation
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 ?_p-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, ?_p-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.
BibTeX - Entry
@InProceedings{cohen_et_al:LIPIcs.ICALP.2023.43,
author = {Cohen, Ilan Reuven and Panigrahi, Debmalya},
title = {{A General Framework for Learning-Augmented Online Allocation}},
booktitle = {50th International Colloquium on Automata, Languages, and Programming (ICALP 2023)},
pages = {43:1--43:21},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-278-5},
ISSN = {1868-8969},
year = {2023},
volume = {261},
editor = {Etessami, Kousha and Feige, Uriel and Puppis, Gabriele},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2023/18095},
URN = {urn:nbn:de:0030-drops-180952},
doi = {10.4230/LIPIcs.ICALP.2023.43},
annote = {Keywords: Algorithms with predictions, Scheduling algorithms, Online algorithms}
}
Keywords: |
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Algorithms with predictions, Scheduling algorithms, Online algorithms |
Collection: |
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50th International Colloquium on Automata, Languages, and Programming (ICALP 2023) |
Issue Date: |
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2023 |
Date of publication: |
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05.07.2023 |