On-line routing of virtual circuits with applications to load balancing and machine scheduling
Journal of the ACM (JACM)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time
Journal of the ACM (JACM)
Random knapsack in expected polynomial time
Journal of Computer and System Sciences - Special issue: STOC 2003
Tradeoffs in worst-case equilibria
Theoretical Computer Science - Approximation and online algorithms
Tight bounds for worst-case equilibria
ACM Transactions on Algorithms (TALG)
Topology matters: smoothed competitiveness of metrical task systems
Theoretical Computer Science
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)
Performance Guarantees of Local Search for Multiprocessor Scheduling
INFORMS Journal on Computing
Average-Case and Smoothed Competitive Analysis of the Multilevel Feedback Algorithm
Mathematics of Operations Research
Worst case and probabilistic analysis of the 2-Opt algorithm for the TSP: extended abstract
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Smoothed analysis: an attempt to explain the behavior of algorithms in practice
Communications of the ACM - A View of Parallel Computing
Tradeoffs and Average-Case Equilibria in Selfish Routing
ACM Transactions on Computation Theory (TOCT)
Local search performance guarantees for restricted related parallel machine scheduling
LATIN'10 Proceedings of the 9th Latin American conference on Theoretical Informatics
A survey of approximation results for local search algorithms
Efficient Approximation and Online Algorithms
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We study popular local search and greedy algorithms for scheduling. The performance guarantee of these algorithms is well understood, but the worst-case lower bounds seem somewhat contrived and it is questionable if they arise in practical applications. To find out how robust these bounds are, we study the algorithms in the framework of smoothed analysis, in which instances are subject to some degree of random noise. While the lower bounds for all scheduling variants with restricted machines are rather robust, we find out that the bounds are fragile for unrestricted machines. In particular, we show that the smoothed performance guarantee of the jump and the lex-jump algorithm are (in contrast to the worst case) independent of the number of machines. They are Θ(φ) and Θ(log φ), respectively, where 1/φ is a parameter measuring the magnitude of the perturbation. The latter immediately implies that also the smoothed price of anarchy is Θ(log φ) for routing games on parallel links. Additionally we show that for unrestricted machines also the greedy list scheduling algorithm has an approximation guarantee of Θ(log φ).