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Distributed Algorithm for Egalitarian Matching between Individuals and Activities with Additively Separable Preferences

机译:具有可分离偏好的个人与活动之间的均等匹配的分布式算法

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We aim at providing a social network such that users form groups to practice together some activities. In this paper, we present a formal framework for coalition formation which is suitable for our usecase. We restrict ourselves to additively separable preferences whose representation is linear with respect to the inputs. We propose here a distributed algorithm which aims at maximizing the egalitarian welfare of the resulting matching. Our experiments shows that: i) this algorithm reaches a better outcome than the classical local search techniques; and ii) the distribution of our algorithm speeds up its runtime.
机译:我们旨在提供一个社交网络,使用户可以组成小组来一起练习一些活动。在本文中,我们提出了一个适合我们用例的正式的联盟形成框架。我们将自己限制为可加可分离的偏好,这些偏好的表示相对于输入是线性的。我们在这里提出一种分布式算法,旨在最大程度地提高匹配结果的平等福利。我们的实验表明:i)该算法比经典的局部搜索技术具有更好的结果; ii)我们算法的分发加快了其运行速度。

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