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A QPTAS for ɛ-Envy-Free Profit-Maximizing Pricing on Line Graphs

Published:16 August 2021Publication History
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Abstract

We consider the problem of pricing edges of a line graph so as to maximize the profit made from selling intervals to single-minded customers. An instance is given by a set E of n edges with a limited supply for each edge, and a set of m clients, where each client specifies one interval of E she is interested in and a budget Bj which is the maximum price she is willing to pay for that interval. An envy-free pricing is one in which every customer is allocated an (possibly empty) interval maximizing her utility. Grandoni and Rothvoss (SIAM J. Comput. 2016) proposed a polynomial-time approximation scheme (PTAS) for the unlimited supply case with running time (nm)O((1/ɛ) 1/ɛ), which was extended to the limited supply case by Grandoni and Wiese (ESA 2019). By utilizing the known hierarchical decomposition of doubling metrics, we give a PTAS with running time (nm)O(1/ ɛ2) for the unlimited supply case. We then consider the limited supply case, and the notion of ɛ-envy-free pricing in which a customer gets an allocation maximizing her utility within an additive error of ɛ. For this case, we develop an approximation scheme with running time (nm)O(log 5/2maxeHe3), where He= Bmax(e)/Bmin(e) is the maximum ratio of the budgets of any two customers demanding edge e. This yields a PTAS in the uniform budget case, and a quasi-PTAS for the general case. The best approximation known, in both cases, for the exact envy-free pricing version is O(log cmax), where cmax is the maximum item supply. Our method is based on the known hierarchical decomposition of doubling metrics, and can be applied to other problems, such as the maximum feasible subsystem problem with interval matrices.

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      • Published in

        cover image ACM Transactions on Economics and Computation
        ACM Transactions on Economics and Computation  Volume 9, Issue 3
        September 2021
        181 pages
        ISSN:2167-8375
        EISSN:2167-8383
        DOI:10.1145/3468852
        Issue’s Table of Contents

        Copyright © 2021 ACM

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        Publication History

        • Published: 16 August 2021
        • Accepted: 1 December 2020
        • Revised: 1 August 2020
        • Received: 1 December 2019
        Published in teac Volume 9, Issue 3

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