Truthful Unsplittable Flow for Large Capacity Networks
Yossi Azar, Iftah Gamzu, Shai Gutner

TL;DR
This paper introduces a new monotone deterministic algorithm for the large capacity unsplittable flow problem in a game theoretic setting, improving approximation ratios and addressing truthfulness constraints.
Contribution
It presents the first monotone primal-dual algorithm achieving near-optimal approximation for the problem, surpassing previous truthful mechanisms and combinatorial algorithms.
Findings
Achieves an approximation ratio of e/(e-1) with a small epsilon gap.
Proves that reasonable iterative path algorithms cannot improve the approximation ratio.
Extends the approach to large capacity multi-unit combinatorial auctions with similar guarantees.
Abstract
In this paper, we focus our attention on the large capacities unsplittable flow problem in a game theoretic setting. In this setting, there are selfish agents, which control some of the requests characteristics, and may be dishonest about them. It is worth noting that in game theoretic settings many standard techniques, such as randomized rounding, violate certain monotonicity properties, which are imperative for truthfulness, and therefore cannot be employed. In light of this state of affairs, we design a monotone deterministic algorithm, which is based on a primal-dual machinery, which attains an approximation ratio of , up to a disparity of away. This implies an improvement on the current best truthful mechanism, as well as an improvement on the current best combinatorial algorithm for the problem under consideration. Surprisingly, we demonstrate that any…
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Taxonomy
TopicsAuction Theory and Applications · Optimization and Search Problems · Mobile Crowdsensing and Crowdsourcing
