  The Nelder-Mead algorithm, a longstanding direct search method for
unconstrained optimization published in 1965, is designed to minimize a
scalar-valued function f of n real variables using only function values,
without any derivative information. Each Nelder-Mead iteration is associated
with a nondegenerate simplex defined by n+1 vertices and their function values;
a typical iteration produces a new simplex by replacing the worst vertex by a
new point. Despite the method's widespread use, theoretical results have been
limited: for strictly convex objective functions of one variable with bounded
level sets, the algorithm always converges to the minimizer; for such functions
of two variables, the diameter of the simplex converges to zero, but examples
constructed by McKinnon show that the algorithm may converge to a nonminimizing
point.
  This paper considers the restricted Nelder-Mead algorithm, a variant that
does not allow expansion steps. In two dimensions we show that, for any
nondegenerate starting simplex and any twice-continuously differentiable
function with positive definite Hessian and bounded level sets, the algorithm
always converges to the minimizer. The proof is based on treating the method as
a discrete dynamical system, and relies on several techniques that are
non-standard in convergence proofs for unconstrained optimization.
