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I have been facinated by the extraordinary explosion in Smoothed Analysis and was struck by a assertion in the paper Smoothed Analysis of Integer Programming. This stated that Integer Linear Programming is in Smoothed P if Polynomially Bounded. This was essential true by the virtue that Integer Programming is Pseudo-polynomial!

The question therefore is:

Does this carry over to other problems universally? In particular what are the constraints?

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    $\begingroup$ Could you elaborate on what is meant by "polynomially bounded" in this context? $\endgroup$ – András Salamon Mar 23 '11 at 11:09
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Integer programming is strongly NP-hard, thus integer programs can in general not be solved in pseudo-polynomial time. The result of Röglin and Vöcking is that, provided that the range of integers that the variables can assume is polynomially bounded, (randomized) pseudo-polynomial solvability is equivalent to polynomial smoothed complexity. Thus, general integer programs do not have polynomial smoothed complexity.

The statement "randomized pseudo-polynomial complexity = polynomial smoothed complexity" is not known to be true in general. For instance, the flip heuristic for Max-Cut runs in pseudo-polynomial time, but it is unknown if a local optimum w.r.t. the flip heuristic can be found with polynomial smoothed complexity (cf. Etscheid and Röglin, SODA 2014).

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