The Nelder-Mead method searches for a local minimum of a function of several variables without using any derivative information, by maintaining a simplex, a shape with one more vertex than the number of dimensions, and repeatedly replacing its worst vertex through operations called reflection, expansion, contraction and shrinkage that move the simplex downhill across the function's surface toward a minimum. John Nelder and Roger Mead published the method in 1965. It remains widely used as a simple, derivative-free optimization technique, though it offers no guarantee of convergence to a true minimum on all functions and can stall on some problem shapes.
Facts
Classification
Design TechniqueHeuristic or Approximation 1 Credited ToJohn Nelder and Roger Mead 1 Connections
In Field
Source Wikipedia: Nelder-Mead method
Uses Design Technique
Sources
1. Wikipedia: Nelder-Mead method
Lead section
was proposed by John Nelder and Roger Mead in 1965
Wikipedia lead/infobox
problems for which derivatives may not be known. However, the Nelder-Mead technique is a heuristic search method that can converge to non-stationary points on problems that can be solved b
In Group: Numerical Search and Equilibrium Methods, Introduction
It is a direct search method (based on function comparison) and is often applied to nonlinear optimization problems for which derivatives may not be known.
In Field: Computational Science, Lead sentence
Nelder-Mead method (also downhill simplex method, amoeba method, or polytope method) is a numerical method used to find a local minimum or maximum of an objective function in a multidimensional space.
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