Average case complete problems
SIAM Journal on Computing
Journal of Computer and System Sciences
Average case complexity under the universal distribution equals worst-case complexity
Information Processing Letters
On the theory of average case complexity
Journal of Computer and System Sciences
Coloring random and semi-random k-colorable graphs
Journal of Algorithms
Heuristics for semirandom graph problems
Journal of Computer and System Sciences
Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time
Journal of the ACM (JACM)
Adding random edges to dense graphs
Random Structures & Algorithms
Random knapsack in expected polynomial time
Journal of Computer and System Sciences - Special issue: STOC 2003
Typical Properties of Winners and Losers [0.2ex] in Discrete Optimization
SIAM Journal on Computing
On smoothed analysis in dense graphs and formulas
Random Structures & Algorithms
Smoothed analysis of integer programming
Mathematical Programming: Series A and B
Solving NP-hard semirandom graph problems in polynomial expected time
Journal of Algorithms
Combinatorics, Probability and Computing
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
Foundations and Trends® in Theoretical Computer Science
Refuting Smoothed 3CNF Formulas
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
On smoothed k-CNF formulas and the Walksat algorithm
SODA '09 Proceedings of the twentieth 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
Beyond Hirsch Conjecture: Walks on Random Polytopes and Smoothed Complexity of the Simplex Method
SIAM Journal on Computing
Smoothed Analysis of Multiobjective Optimization
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
Pareto optimal solutions for smoothed analysts
Proceedings of the forty-third annual ACM symposium on Theory of computing
Smoothed Analysis of the k-Means Method
Journal of the ACM (JACM)
On Smoothed Analysis of Quicksort and Hoare’s Find
Algorithmica
Smoothed analysis of left-to-right maxima with applications
ACM Transactions on Algorithms (TALG)
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Smoothed analysis is a new way of analyzing algorithms introduced by Spielman and Teng (J. ACM, 2004). Classical methods like worst-case or average-case analysis have accompanying complexity classes, like P and Avg−P, respectively. While worst-case or average-case analysis give us a means to talk about the running time of a particular algorithm, complexity classes allows us to talk about the inherent difficulty of problems. Smoothed analysis is a hybrid of worst-case and average-case analysis and compensates some of their drawbacks. Despite its success for the analysis of single algorithms and problems, there is no embedding of smoothed analysis into computational complexity theory, which is necessary to classify problems according to their intrinsic difficulty. We propose a framework for smoothed complexity theory, define the relevant classes, and prove some first results.