arXiv:1402.0423cs.DS
A Bound on the Expected Optimality of Random Feasible Solutions to Combinatorial Optimization Problems
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Abstract
This paper investigates and bounds the expected solution quality of combinatorial optimization problems when feasible solutions are chosen at random. Loose general bounds are discovered, as well as families of combinatorial optimization problems for which random feasible solutions are expected to be a constant factor of optimal. One implication of this result is that, for graphical problems, if the average edge weight in a feasible solution is sufficiently small, then any randomly chosen feasible solution to the problem will be a constant factor of optimal. For example, under certain well-defined circumstances, the expected constant of approximation of a randomly chosen feasible solution to the Steiner network problem is bounded above by 3. Empirical analysis supports these bounds and actually suggest that they might be tightened.
This seems to be a specific case of the more general principle of "concentration of measure". Terence Tao at [1] has some useful notes. From a quick scan of the paper at http://arxiv.org/pdf/1402.0423v1.pdf, Theorem 1 seems to essentially be a CoM result for the order statistics…
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