MinFinder: locating all the local minima of a function

DOI10.1016/j.cpc.2005.10.001zbMath1196.90087OpenAlexW2124514594MaRDI QIDQ709933

Ioannis G. Tsoulos, Isaac E. Lagaris

Publication date: 18 October 2010

Published in: Computer Physics Communications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cpc.2005.10.001

zbMATH Keywords

global optimizationMonte Carloclusteringstochastic methodsregion of attraction\texttt{MinFinder}

Mathematics Subject Classification ID

Stochastic programming (90C15) Software, source code, etc. for problems pertaining to operations research and mathematical programming (90-04)

Related Items

Non-monotone derivative-free algorithm for solving optimization models with linear constraints: extensions for solving nonlinearly constrained models via exact penalty methods ⋮ Approximating the objective function's gradient using perceptrons for constrained minimization with application in drag reduction ⋮ A stochastic optimization algorithm for analyzing planar central and balanced configurations in the \(n\)-body problem ⋮ On locating all roots of systems of nonlinear equations inside bounded domain using global optimization methods ⋮ MinFinder ⋮ Towards ``Ideal multistart. A stochastic approach for locating the minima of a continuous function inside a bounded domain

Uses Software

• Algorithm 829
• simannf90

Cites Work

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• Optimization by Simulated Annealing
• A tolerant algorithm for linearly constrained optimization calculations
• Handbook of test problems in local and global optimization
• Topographical multilevel single linkage
• A hybrid algorithm for identifying global and local minima when optimizing functions with many minima.
• Tabu search applied to global optimization
• A stochastic method for global optimization
• Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm—Corrigenda for this article is available here
• Stochastic global optimization methods part I: Clustering methods
• Stochastic global optimization methods part II: Multi level methods
• Algorithm 829
• A new approach to variable metric algorithms

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