Probabilistic analysis of the multidimensional knapsack problem
Mathematics of Operations Research
On the average number of maxima in a set of vectors
Information Processing Letters
Average-case analysis of off-line and on-line knapsack problems
Journal of Algorithms - Special issue on SODA '95 papers
On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Proceedings of the 17th International Conference on Data Engineering
On the approximability of trade-offs and optimal access of Web sources
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
On finding the exact solution of a zero-one knapsack problem
STOC '84 Proceedings of the sixteenth annual ACM symposium on Theory of computing
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
Typical Properties of Winners and Losers [0.2ex] in Discrete Optimization
SIAM Journal on Computing
Multicriteria Optimization
Smoothed analysis of integer programming
Mathematical Programming: Series A and B
Decision-making based on approximate and smoothed Pareto curves
Theoretical Computer Science
The Smoothed Number of Pareto Optimal Solutions in Bicriteria Integer Optimization
IPCO '07 Proceedings of the 12th international conference on Integer Programming and Combinatorial Optimization
Smoothed Analysis of Multiobjective Optimization
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
Approximation of multiobjective optimization problems
Approximation of multiobjective optimization problems
Lower bounds for the smoothed number of pareto optimal solutions
TAMC'11 Proceedings of the 8th annual conference on Theory and applications of models of computation
Improved smoothed analysis of multiobjective optimization
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
MFCS'12 Proceedings of the 37th international conference on Mathematical Foundations of Computer Science
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Consider an optimization problem with n binary variables and d+1 linear objective functions. Each valid solution x ∈{0,1}n gives rise to an objective vector in Rd+1, and one often wants to enumerate the Pareto optima among them. In the worst case there may be exponentially many Pareto optima; however, it was recently shown that in (a generalization of) the smoothed analysis framework, the expected number is polynomial in~n. Unfortunately, the bound obtained had a rather bad dependence on d; roughly ndd. In this paper we show a significantly improved bound of n2d. Our proof is based on analyzing two algorithms. The first algorithm, on input a Pareto optimal x, outputs a "testimony" containing clues about x's objective vector, x's coordinates, and the region of space B in which x's objective vector lies. The second algorithm can be regarded as a speculative execution of the first --- it can uniquely reconstruct x from the testimony's clues and just some of the probability space's outcomes. The remainder of the probability space's outcomes are just enough to bound the probability that x's objective vector falls into the region B.