Geometric Convergence of Algorithms in Gambling Theory

In the Dubins and Savage theory of gambling, backward induction provides an algorithm for calculating the optimal return when the gambling problem is leavable. A relatively new algorithm works for nonleavable problems. We show that these algorithms converge geometrically fast for finite gambling problems. Our argument also provides a much simpler proof of convergence for the nonleavable case.