Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Approximation Schemes for Covering and Scheduling on Related Machines
APPROX '98 Proceedings of the International Workshop on Approximation Algorithms for Combinatorial Optimization
ESA '97 Proceedings of the 5th Annual European Symposium on Algorithms
Truthful approximation mechanisms for restricted combinatorial auctions: extended abstract
Eighteenth national conference on Artificial intelligence
Strategyproof cost-sharing mechanisms for set cover and facility location games
Proceedings of the 4th ACM conference on Electronic commerce
Truthful Mechanisms for One-Parameter Agents
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Approximation schemes for scheduling and covering on unrelated machines
Theoretical Computer Science
ACM Transactions on Algorithms (TALG)
Truthful Approximation Mechanisms for Scheduling Selfish Related Machines
Theory of Computing Systems
Truthful Approximation Schemes for Single-Parameter Agents
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Fast monotone 3-approximation algorithm for scheduling related machines
ESA'05 Proceedings of the 13th annual European conference on Algorithms
The exact LPT-bound for maximizing the minimum completion time
Operations Research Letters
A polynomial-time approximation scheme for maximizing the minimum machine completion time
Operations Research Letters
A truthful constant approximation for maximizing the minimum load on related machines
WINE'10 Proceedings of the 6th international conference on Internet and network economics
The cost of selfishness for maximizing the minimum load on uniformly related machines
Journal of Combinatorial Optimization
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We consider the problem of maximizing the minimum load (completion time) for machines that are controlled by selfish agents, who are only interested in maximizing their own profit. Unlike the classical load balancing problem, this problem has not been considered for selfish agents until now. The goal is to design a truthful mechanism, i.e., one in which all users have an incentive to tell the truth about the speeds of their machines. This then allows us to find good job assignments. It is known that this requires monotone approximation algorithms, in which the amount of work assigned to an agent does not increase if its bid (claimed cost per unit work) increases. For a constant number of machines, m, we show a monotone polynomial-time approximation scheme (PTAS) with running time that is linear in the number of jobs. It uses a new technique for reducing the number of jobs while remaining close to the optimal solution. We use an FPTAS for the classical problem, i.e., where no selfish agents are involved, to give a monotone FPTAS. Additionally, we give a monotone approximation algorithm with approximation ratio min(m,(2+@e)s"1/s"m) where @e0 can be chosen arbitrarily small and s"i is the (real) speed of machine i. Finally we give improved results for two machines.