Quasiconvex Analysis of Multivariate Recurrence Equations for Backtracking Algorithms
We consider a class of multivariate recurrences frequently arising in the worst-case analysis of Davis-Putnam-style exponential-time backtracking algorithms for NP-hard problems. We describe a technique for proving asymptotic upper bounds on these recurrences, by using a suitable weight function to reduce the problem to that of solving univariate linear recurrences; show how to use quasiconvex programming to determine the weight function yielding the smallest upper bound; and prove that the resulting upper bounds are within a polynomial factor of the true asymptotics of the recurrence. We develop and implement a multiple-gradient descent algorithm for the resulting quasiconvex programs, using a real-number arithmetic package for guaranteed accuracy of the computed worst-case time bounds.
multivariate recurrences backtracking algorithms worst-case analysis quasiconvex programming automated analysis of algorithms davis-putnam procedures method of feasible directions