Mathematical Programming

, Volume 160, Issue 1–2, pp 407–431

# Average case polyhedral complexity of the maximum stable set problem

Full Length Paper

## Abstract

We study the minimum number of constraints needed to formulate random instances of the maximum stable set problem via linear programs (LPs), in two distinct models. In the uniform model, the constraints of the LP are not allowed to depend on the input graph, which should be encoded solely in the objective function. There we prove a $$2^{\Omega (n{/}\log n)}$$ lower bound with probability at least $$1 - 2^{-2^n}$$ for every LP that is exact for a randomly selected set of instances; each graph on at most n vertices being selected independently with probability $$p \geqslant 2^{- \left( {\begin{array}{c}n/4\\ 2\end{array}}\right) + n}$$. In the non-uniform model, the constraints of the LP may depend on the input graph, but we allow weights on the vertices. The input graph is sampled according to the G(np) model. There we obtain upper and lower bounds holding with high probability for various ranges of p. We obtain a super-polynomial lower bound all the way from $$p = \Omega \left( \frac{\log ^{6+\varepsilon } n}{n} \right)$$ to $$p = o\left( \frac{1}{\log n} \right)$$. Our upper bound is close to this as there is only an essentially quadratic gap in the exponent, which currently also exists in the worst-case model. Finally, we state a conjecture that would close this gap, both in the average-case and worst-case models.

## Mathematics Subject Classification

Primary 68Q17 Secondary 05C69 05C80 90C05

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© Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2016

## Authors and Affiliations

• Gábor Braun
• 1
Email author
• Samuel Fiorini
• 2
• Sebastian Pokutta
• 1
1. 1.ISyE, Georgia Institute of TechnologyAtlantaUSA
2. 2.Department of MathematicsUniversité libre de BruxellesBrusselsBelgium