2007Romanian Journal of Economic ForecastingRequires access

Applying Nelder Mead’s Optimization Algorithm for Multiple Global Minima

Ştefan Ştefânescu

Open publisher page 9 citations

Abstract

The iterative deterministic optimization method could not more find multiple global minima of a given objective function ( [6] ). Generally, the probabilistic optimization algorithms have not this restrictive behaviour, to determine only a single global minimum point. In this context we’ll prove experimentally that Nelder-Mead’s heuristic procedure can detect successfully multiple global extremal points.

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What this paper is about

The iterative deterministic optimization method could not more find multiple global minima of a given objective function ( [6] ). Generally, the probabilistic optimization algorithms have not this restrictive behaviour, to determine only a single global minimum point. In this context we’ll prove experimentally that Nelder-Mead’s heuristic procedure can detect successfully multiple global extremal points.

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OpenAlex reports 9 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The iterative deterministic optimization method could not more find multiple global minima of a given objective function ( [6] ). Generally, the probabilistic optimization algorithms have not this restrictive behaviour, to determine only a single global minimum point. In this context we’ll prove experimentally that Nelder-Mead’s heuristic procedure can detect successfully multiple global extremal points.

Key concepts: Maxima and minima, Global optimization, Mathematical optimization, Heuristic, Context (archaeology), Probabilistic logic, Point (geometry), Algorithm

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