Testing Unconstrained Optimization Software
ACM Transactions on Mathematical Software (TOMS)
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Convergence of the Nelder--Mead Simplex Method to a Nonstationary Point
SIAM Journal on Optimization
SIAM Journal on Optimization
Fortified-Descent Simplicial Search Method: A General Approach
SIAM Journal on Optimization
A convergent variant of the Nelder-Mead algorithm
Journal of Optimization Theory and Applications
An interactive approach for solving multi-objective optimization problems (interactive computer, nelder-mead simplex algorithm, graphics)
Multidirectional search: a direct search algorithm for parallel machines
Multidirectional search: a direct search algorithm for parallel machines
A simplex-based numerical framework for simple and efficient robust design optimization
Computational Optimization and Applications
Generalized dynamical fuzzy model for identification and prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper, we first prove that the expansion and contraction steps of the Nelder-Mead simplex algorithm possess a descent property when the objective function is uniformly convex. This property provides some new insights on why the standard Nelder-Mead algorithm becomes inefficient in high dimensions. We then propose an implementation of the Nelder-Mead method in which the expansion, contraction, and shrink parameters depend on the dimension of the optimization problem. Our numerical experiments show that the new implementation outperforms the standard Nelder-Mead method for high dimensional problems.