  We give a deterministic, polynomial-time algorithm for approximately counting
the number of {0,1}-solutions to any instance of the knapsack problem. On an
instance of length n with total weight W and accuracy parameter eps, our
algorithm produces a (1 + eps)-multiplicative approximation in time poly(n,log
W,1/eps). We also give algorithms with identical guarantees for general integer
knapsack, the multidimensional knapsack problem (with a constant number of
constraints) and for contingency tables (with a constant number of rows).
Previously, only randomized approximation schemes were known for these problems
due to work by Morris and Sinclair and work by Dyer.
  Our algorithms work by constructing small-width, read-once branching programs
for approximating the underlying solution space under a carefully chosen
distribution. As a byproduct of this approach, we obtain new query algorithms
for learning functions of k halfspaces with respect to the uniform distribution
on {0,1}^n. The running time of our algorithm is polynomial in the accuracy
parameter eps. Previously even for the case of k=2, only algorithms with an
exponential dependence on eps were known.

Data Structures and Algorithms;Computational Complexity;Machine Learning