Online Graph Algorithms with Predictions
Yossi Azar, Debmalya Panigrahi, Noam Touitou

TL;DR
This paper introduces a new framework for online graph algorithms with predictions, defining a novel metric error with outliers, and provides tight competitive bounds for classical problems based on this error.
Contribution
It presents the first general framework for combining offline and online algorithms with predictions, and introduces a new error measure capturing multiple prediction inaccuracies.
Findings
Defined metric error with outliers for graph problems.
Developed a general framework for online algorithms with predictions.
Achieved tight competitive ratio bounds for several classical graph problems.
Abstract
Online algorithms with predictions is a popular and elegant framework for bypassing pessimistic lower bounds in competitive analysis. In this model, online algorithms are supplied with future predictions, and the goal is for the competitive ratio to smoothly interpolate between the best offline and online bounds as a function of the prediction error. In this paper, we study online graph problems with predictions. Our contributions are the following: * The first question is defining prediction error. For graph/metric problems, there can be two types of error, locations that are not predicted, and locations that are predicted but the predicted and actual locations do not coincide exactly. We design a novel definition of prediction error called metric error with outliers to simultaneously capture both types of errors, which thereby generalizes previous definitions of error that only…
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Taxonomy
TopicsOptimization and Search Problems · Complexity and Algorithms in Graphs · Advanced Bandit Algorithms Research
