Optimizing the cost of evaluating a polynomial is a classic problem in
computer science. For polynomials in one variable, Horner's method provides a
scheme for producing a computationally efficient form. For multivariate
polynomials it is possible to generalize Horner's method, but this leaves
freedom in the order of the variables. Traditionally, greedy schemes like
most-occurring variable first are used. This simple textbook algorithm has
given remarkably efficient results. Finding better algorithms has proved
difficult. In trying to improve upon the greedy scheme we have implemented
Monte Carlo tree search, a recent search method from the field of artificial
intelligence. This results in better Horner schemes and reduces the cost of
evaluating polynomials, sometimes by factors up to two.Comment: 5 page