Implementing the Nelder-Mead simplex algorithm with adaptive parameters

This paper proves that the expansion and contraction steps of the Nelder-Mead simplex algorithm possess a descent property when the objective function is uniformly convex, and proposes an implementation in which the expansion, contraction, and shrink parameters depend on the dimension of the optimization problem.