MinFinder: Locating all the local minima of a function

Published: 15 January 2006| Version 1 | DOI: 10.17632/xvn3z2d8db.1
Ioannis G. Tsoulos, Isaac E. Lagaris


Abstract 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. Title of program: MinFinder Catalogue Id: ADWU_v1_0 Nature of problem A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques can be trapped in any local minimum. Global optimization is then the appropriate tool. For example, solving a non - linear system of equations via optimization, one may encounter many local minima that do no ... Versions of this program held in the CPC repository in Mendeley Data ADWU_v1_0; MinFinder; 10.1016/j.cpc.2005.10.001 ADWU_v2_0; MinFinder v2.0; 10.1016/j.cpc.2008.04.016 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)



Computational Physics, Computational Method