MinFinder

MinFinder: locating all the local minima of a function. A new stochastic clustering algorithm is introduced that aims to locate all the local minima of a multidimensional continuous and differentiable function inside a bounded domain. The accompanying software (MinFinder) is written in ANSI C++. However, the user may code his objective function either in C++, C or Fortran 77. We compare the performance of this new method to the performance of Multistart and Topographical Multilevel Single Linkage Clustering on a set of benchmark problems.

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References in zbMATH (referenced in 6 articles , 2 standard articles )

Showing results 1 to 6 of 6.
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  1. García-Palomares, Ubaldo M.: Non-monotone derivative-free algorithm for solving optimization models with linear constraints: extensions for solving nonlinearly constrained models via exact penalty methods (2020)
  2. Kocuk, Burak; Altınel, İ. Kuban; Aras, Necati: Approximating the objective function’s gradient using perceptrons for constrained minimization with application in drag reduction (2015)
  3. Tsoulos, I. G.; Stavrakoudis, Athanassios: On locating all roots of systems of nonlinear equations inside bounded domain using global optimization methods (2010)
  4. Voglis, C.; Lagaris, I. E.: Towards “Ideal multistart”. A stochastic approach for locating the minima of a continuous function inside a bounded domain (2009)
  5. Tsoulos, Ioannis G.; Lagaris, Isaac E.: MinFinder v2.0: an improved version of minfinder (2008)
  6. Tsoulos, Ioannis G.; Lagaris, Isaac E.: MinFinder: locating all the local minima of a function (2006)