Randomized Optimization: a Probabilistic Analysis - Jean Cardinal; Stefan Langerman; Guy Louchard (Discrete Mathematics & Theoretical Computer Science, 2007)
In 1999, Chan proposed an algorithm to solve a given optimization problem: express the solution as the minimum of the solutions of several subproblems and apply the classical randomized algorithm for finding the minimum of $r$ numbers. If the decision versions of the subproblems are easier to solve than the subproblems themselves, then a faster algorithm for the optimization problem may be obtained with randomization. In this paper we present a precise probabilistic analysis of Chan's technique.