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In this paper, we give the first constant approximation algorithm for the lower bounded facility location (LBFL) problem with general lower bounds. Prior to our work, such algorithms were only known for the special case where all facilities…

数据结构与算法 · 计算机科学 2018-05-08 Shi Li

We propose a new distributed optimization algorithm for solving a class of constrained optimization problems in which (a) the objective function is separable (i.e., the sum of local objective functions of agents), (b) the optimization…

最优化与控制 · 数学 2021-06-16 Van Sy Mai , Richard J. La , Tao Zhang , Abdella Battou

In this paper, we study of the $m$-Capacitated Facility Location Problem ($m$-CFLP) on the line from a Bayesian Mechanism Design perspective and propose a novel class of mechanisms: the \textit{Extended Ranking Mechanisms} (ERMs). We first…

计算机科学与博弈论 · 计算机科学 2024-06-18 Gennaro Auricchio , Jie Zhang , Mengxiao Zhang

We study mechanisms for candidate selection that seek to minimize the social cost, where voters and candidates are associated with points in some underlying metric space. The social cost of a candidate is the sum of its distances to each…

计算机科学与博弈论 · 计算机科学 2015-12-29 Michal Feldman , Amos Fiat , Iddan Golomb

We propose a numerical algorithm for computing approximately optimal solutions of the matching for teams problem. Our algorithm is efficient for problems involving large number of agent categories and allows for non-discrete agent type…

最优化与控制 · 数学 2025-05-27 Ariel Neufeld , Qikun Xiang

In this article, we study the optimization of resource distributions in a one-dimensional logistic diffusive model. The goal is to determine a distribution on a bounded one-dimensional domain that maximizes the total population at…

偏微分方程分析 · 数学 2025-11-21 Junyoung Heo , Yubin Lee

We study Nash equilibria in strategic facility location games where clients are located in an arbitrary metric space. Specifically, there are $n$ clients, and the goal is to choose a facility from a set of given locations, so that the total…

计算机科学与博弈论 · 计算机科学 2026-04-21 Yue Gruszecki , Elliot Anshelevich

We consider allocation of a resource to multiple interested users with a constraint that if the resource is allocated to user $i$ then it can not be allocated simultaneously to a predefined set of users $\cS_i$ . This scenario arises in…

计算机科学与博弈论 · 计算机科学 2018-11-29 Indu Yadav , Prasanna Chaporkar , Abhay Karandikar

This paper addresses the problem of collaboratively satisfying long-term spatial constraints in multi-agent systems. Each agent is subject to spatial constraints, expressed as inequalities, which may depend on the positions of other agents…

系统与控制 · 电气工程与系统科学 2026-03-23 Farhad Mehdifar , Mani H. Dhullipalla , Charalampos P. Bechlioulis , Dimos V. Dimarogonas

Motivated by group-project distribution, we introduce and study stable matching under the constraint of applicants needing to share a location to be matched with the same institute, which we call the Location-Restricted Stable Matching…

数据结构与算法 · 计算机科学 2025-08-05 Garret Castro

Strategy-proof mechanisms are widely used in market design. In an abstract allocation framework where outside options are available to agents, we obtain two results for strategy-proof mechanisms. They provide a unified foundation for…

理论经济学 · 经济学 2021-01-05 Jun Zhang

We consider a facility location problem, where the objective is to ``disperse'' a number of facilities, i.e., select a given number k of locations from a discrete set of n candidates, such that the average distance between selected…

数据结构与算法 · 计算机科学 2007-05-23 Sandor P. Fekete , Henk Meijer

Our work is devoted to the metric facility location problem and addresses the selfish behavior of the players. It contributes to the line of work initiated by Procaccia and Tennenholtz [EC09] on approximate mechanism design without money.…

计算机科学与博弈论 · 计算机科学 2012-02-10 Nick Gravin , Dominik Scheder

An important challenge in robust machine learning is when training data is provided by strategic sources who may intentionally report erroneous data for their own benefit. A line of work at the intersection of machine learning and mechanism…

计算机科学与博弈论 · 计算机科学 2024-12-24 Eric Balkanski , Cherlin Zhu

We initiate the study of the capacity constrained facility location problem from a mechanism design perspective. The capacity constrained setting leads to a new strategic environment where a facility serves a subset of the population, which…

计算机科学与博弈论 · 计算机科学 2019-02-19 Haris Aziz , Hau Chan , Barton E. Lee , David C. Parkes

We study an abstract optimal auction problem for a single good or service. This problem includes environments where agents have budgets, risk preferences, or multi-dimensional preferences over several possible configurations of the good…

计算机科学与博弈论 · 计算机科学 2012-03-23 Saeed Alaei , Hu Fu , Nima Haghpanah , Jason Hartline , Azarakhsh Malekian

The augmentation of algorithms with predictions of the optimal solution, such as from a machine-learning algorithm, has garnered significant attention in recent years, particularly in facility location problems. Moving beyond the…

计算机科学与博弈论 · 计算机科学 2026-02-05 Haris Aziz , Yuhang Guo , Alexander Lam , Houyu Zhou

We first show that a better analysis of the algorithm for The Two-Sage Stochastic Facility Location Problem from Srinivasan \cite{sri07} and the algorithm for The Robust Fault Tolerant Facility Location Problem from Byrka et al \cite{bgs10}…

数据结构与算法 · 计算机科学 2013-05-13 Shuxin Cai , Wenguo Yang , Yaohua Tang

In the context of search and rescue, we consider the problem of mission planning for heterogeneous teams that can include human, robotic, and animal agents. The problem is tackled using a mixed integer mathematical programming formulation…

机器人学 · 计算机科学 2013-12-18 Eduardo Feo Flushing , Luca M. Gambardella , Gianni A. Di Caro

In this paper, we study a novel Stochastic Network Utility Maximization (NUM) problem where the utilities of agents are unknown. The utility of each agent depends on the amount of resource it receives from a network operator/controller. The…

机器学习 · 计算机科学 2020-06-18 Arun Verma , Manjesh K. Hanawal