  Given two bounded convex sets $X\subseteq\RR^m$ and $Y\subseteq\RR^n,$
specified by membership oracles, and a continuous convex-concave function
$F:X\times Y\to\RR$, we consider the problem of computing an $\eps$-approximate
saddle point, that is, a pair $(x^*,y^*)\in X\times Y$ such that $\sup_{y\in Y}
F(x^*,y)\le \inf_{x\in
  X}F(x,y^*)+\eps.$ Grigoriadis and Khachiyan (1995) gave a simple randomized
variant of fictitious play for computing an $\eps$-approximate saddle point for
matrix games, that is, when $F$ is bilinear and the sets $X$ and $Y$ are
simplices. In this paper, we extend their method to the general case. In
particular, we show that, for functions of constant "width", an
$\eps$-approximate saddle point can be computed using
$O^*(\frac{(n+m)}{\eps^2}\ln R)$ random samples from log-concave distributions
over the convex sets $X$ and $Y$. It is assumed that $X$ and $Y$ have inscribed
balls of radius $1/R$ and circumscribing balls of radius $R$. As a consequence,
we obtain a simple randomized polynomial-time algorithm that computes such an
approximation faster than known methods for problems with bounded width and
when $\eps \in (0,1)$ is a fixed, but arbitrarily small constant. Our main tool
for achieving this result is the combination of the randomized fictitious play
with the recently developed results on sampling from convex sets.
