Journal of the ACM (JACM)
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Algorithm Design
Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series)
Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series)
Theoretical Aspects of Local Search (Monographs in Theoretical Computer Science. An EATCS Series)
Theoretical Aspects of Local Search (Monographs in Theoretical Computer Science. An EATCS Series)
Approximation via cost sharing: Simpler and better approximation algorithms for network design
Journal of the ACM (JACM)
Strong equilibrium in cost sharing connection games
Proceedings of the 8th ACM conference on Electronic commerce
Cost-Sharing Mechanisms for Network Design
Algorithmica
On the parameterized complexity of multiple-interval graph problems
Theoretical Computer Science
Quantifying inefficiency in cost-sharing mechanisms
Journal of the ACM (JACM)
The Price of Stability for Network Design with Fair Cost Allocation
SIAM Journal on Computing
On the Value of Coordination in Network Design
SIAM Journal on Computing
Local search: is brute-force avoidable?
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Local search: is brute-force avoidable?
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Designing Network Protocols for Good Equilibria
SIAM Journal on Computing
Computer Science Review
The parameterized complexity of k-flip local search for SAT and MAX SAT
Discrete Optimization
Non-Cooperative Multicast and Facility Location Games
IEEE Journal on Selected Areas in Communications
Parameterized Complexity
Hi-index | 0.00 |
A multicast game is a network design game modelling how selfish non-cooperative agents build and maintain one-to-many network communication. There is a special source node and a collection of agents located at corresponding terminals. Each agent is interested in selecting a route from the special source to its terminal minimizing the cost. The mutual influence of the agents is determined by a cost sharing mechanism, which evenly splits the cost of an edge among all the agents using it for routing. The existence of a Nash equilibrium for the game was previously established by the means of Rosenthal potential. Anshelevich et al. [FOCS 2004, SICOMP 2008] introduced a measure of quality of the best Nash equilibrium, the price of stability, as the ratio of its cost to the optimum network cost. While Rosenthal potential is a reasonable measure of the quality of Nash equilibra, finding a Nash equilibrium minimizing this potential is NP-hard. In this paper we provide several algorithmic and complexity results on finding a Nash equilibrium minimizing the value of Rosenthal potential. Let n be the number of agents and G be the communication network. We show that For a given strategy profile s and integer k≥1, there is a local search algorithm which in time nO(k) ·|G|O(1) finds a better strategy profile, if there is any, in a k-exchange neighbourhood of s. In other words, the algorithm decides if Rosenthal potential can be decreased by changing strategies of at most k agents; The running time of our local search algorithm is essentially tight: unless FPT=W[1], for any function f(k), searching of the k-neighbourhood cannot be done in time f(k)·|G|O(1). The key ingredient of our algorithmic result is a subroutine that finds an equilibrium with minimum potential in 3n ·|G|O(1) time. In other words, finding an equilibrium with minimum potential is fixed-parameter tractable when parameterized by the number of agents.