منابع مشابه
Grid Restrained Nelder-Mead Algorithm
Probably the most popular algorithm for unconstrained minimization for problems of moderate dimension is the Nelder-Mead algorithm published in 1965. Despite its age only limited convergence results exist. Several counterexamples can be found in the literature for which the algorithm performs badly or even fails. A convergent variant derived from the original Nelder-Mead algorithm is presented....
متن کاملGeometric Generalization of the Nelder-Mead Algorithm
The Nelder-Mead Algorithm (NMA) is an almost half-century old method for numerical optimization, and it is a close relative of Particle Swarm Optimization (PSO) and Differential Evolution (DE). Geometric Particle Swarm Optimization (GPSO) and Geometric Differential Evolution (GDE) are recently introduced formal generalization of traditional PSO and DE that apply naturally to both continuous and...
متن کاملConvergence of the Restricted Nelder-Mead Algorithm in Two Dimensions
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 produce...
متن کاملImplementing the Nelder-Mead simplex algorithm with adaptive parameters
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, contra...
متن کاملNelder-Mead Evolutionary Hybrid Algorithms
Real world optimization problems are often too complex to be solved through analytic means. Evolutionary algorithms are a class of algorithms that borrow paradigms from nature to address them. These are stochastic methods of optimization that maintain a population of individual solutions, which correspond to points in the search space of the problem. These algorithms have been immensely popular...
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ژورنال
عنوان ژورنال: Scholarpedia
سال: 2009
ISSN: 1941-6016
DOI: 10.4249/scholarpedia.2928