Online contention resolution schemes with applications to Bayesian selection problems

Moran Feldman, Ola Svensson, Rico Zenklusen

פרסום מחקרי: פרסום בכתב עתמאמרביקורת עמיתים


We introduce a new rounding technique designed for online optimization problems, which is related to contention resolution schemes, a technique initially introduced in the context of submodular function maximization. Our rounding technique, which we call online contention resolution schemes (OCRSs), is applicable to many online selection problems, including Bayesian online selection, oblivious posted pricing mechanisms, and stochastic probing models. It allows for handling a wide set of constraints and shares many strong properties of offline contention resolution schemes. In particular, OCRSs for different constraint families can be combined to obtain an OCRS for their intersection. Moreover, we can approximately maximize submodular functions in the online settings we consider. We thus get a broadly applicable framework for several online selection problems, which improves on previous approaches in terms of the types of constraints that can be handled, the objective functions that can be dealt with, and the assumptions on the strength of the adversary. Furthermore, we resolve two open problems from the literature; namely, we present the first constant-factor constrained oblivious posted price mechanism for matroid constraints and the first constant-factor algorithm for weighted stochastic probing with deadlines.

שפה מקוריתאנגלית
עמודים (מ-עד)255-300
מספר עמודים46
כתב עתSIAM Journal on Computing
מספר גיליון2
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 2021
פורסם באופן חיצוניכן

הערה ביבליוגרפית

Publisher Copyright:
© 2021 Moran Feldman, Ola Svensson, and Rico Zenklusen

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