Proving Conditional Randomness using The Principle of Deferred Decisions

In order to prove that a certain property holds asymptotically for a restricted class of objects such as formulas or graphs, one may apply a heuristic on a random element of the class, and then prove by probabilistic analysis that the heuristic succeeds with high probability. This method has been used to establish lower bounds on thresholds for desirable properties such as satisfiability and colorability: lower bounds for the 3-SAT threshold were discussed briefly in the previous chapter. The probabilistic analysis depends on analyzing the mean trajectory of the heuristic—as we have seen in Cocco et al. [3]—and in parallel, showing that in the asymptotic limit the trajectory’s properties are strongly concentrated about their mean. However, the mean trajectory analysis requires that certain random characteristics of the heuristic’s starting sample are retained throughout the trajectory. We propose a methodology in this chapter to determine the conditional that should be imposed on a random object, such as a conjunctive normal form (CNF)