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- W2251504306 abstract "This chapter explores the probabilistic analysis of algorithms that has become an active research area. It reviews some representative results that are known for problems defined in the Euclidean plane. The chapter examines the fertile area of optimization problems defined on graphs and networks and presents a short digression on modes of stochastic convergence, an essential concept if one wants to analyze the notion of a random variable such as the error of a heuristic going to 0 with increasing problem size. The probabilistic analysis of heuristic method performance is quite difficult; each step in the algorithm conditions the probability distribution encountered in the succeeding steps in a complicated fashion. As the difference of two independent uniformly distributed random variables follows a triangular distribution, there is no distributional invariance throughout the steps of this method and yet such invariance is an essential prerequisite for a successful analysis. One way to overcome this kind of obstacle is to change the algorithm so that the value produced is not affected but its modified behavior is analyzed." @default.
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- W2251504306 date "1987-01-01" @default.
- W2251504306 modified "2023-09-27" @default.
- W2251504306 title "Probabilistic Analysis of Algorithms" @default.
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- W2251504306 doi "https://doi.org/10.1016/s0304-0208(08)73241-9" @default.
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