The Nelder-Mead simplex method does not use any derivatives and does not assume that the objective function has continuous derivatives. The objective function itself needs to be continuous.

The mission to improve the widely used simplex-method algorithm showed instead why it works so well.

Compared with Levenberg Marquardt, the local and initial value sensitive algorithm, Simplex, a global algorithm, is the only robust and easy method which is irrelevant to initial input value.

MIT, Cambridge] and the original simplex method with the most-negative-reduced-cost pivoting rule of Dantzig are strongly polynomial-time algorithms for solving the Markov decision problem (MDP) with ...

Quart. 2 (4) 269-275]. Using this approach, we give a simple geometric explanation for a number of examples of cycling in the simplex method, including Hoffman's original example [Hoffman, A. 1953.

YOU MIGHT not have heard of the algorithm that runs the world. Few people have, though it can determine much that goes on in our day-to-day lives: the food we have to eat, our schedule at work ...

In the empirical Bayesian method of Xu (2007), estimation of variance components is achieved by repeated callings of the Nelder and Mead (1965) simplex algorithm.