An Average Analysis of Backtracking on Random Constraint Satisfaction Problems

Abstract

In this paper we propose a random CSP model, called Model GB, which is a natural generalization of standard Model B. This paper considers Model GB in the case where each constraint is easy to satisfy. In this case Model GB exhibits non-trivial behaviour (not trivially satisfiable or unsatisfiable) as the number of variables approaches infinity. A detailed analysis to obtain an asymptotic estimate (good to 1po(1)) of the average number of nodes in a search tree used by the backtracking algorithm on Model GB is also presented. It is shown that the average number of nodes required for finding all solutions or proving that no solution exists grows exponentially with the number of variables. So this model might be an interesting distribution for studying the nature of hard instances and evaluating the performance of CSP algorithms. In addition, we further investigate the behaviour of the average number of nodes as i>r (the ratio of constraints to variables) varies. The results indicate that as i>r increases, random CSP instances get easier and easier to solve, and the base for the average number of nodes that is exponential in i>n tends to 1 as i>r approaches infinity. Therefore, although the average number of nodes used by the backtracking algorithm on random CSP is exponential, many CSP instances will be very easy to solve when i>r is sufficiently large.