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Smoothed Analysis of the Perceptron Algorithm for Linear Programming

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journal contribution
posted on 01.01.1965, 00:00 by Avrim Blum, John Dunagan
The smoothed complexity [1] of an algorithm is the expected running time of the algorithm on an arbitrary instance under a random perturbation. It was shown recently that the simplex algorithm has polynomial smoothed complexity. We show that a simple greedy algorithm for linear programming, the perceptron algorithm, also has polynomial smoothed complexity, in a high probability sense; that is, the running time is polynomial with high probability over the random perturbation.


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