Package: neighbours 0.1-3

neighbours: Neighbourhood Functions for Local-Search Algorithms

Neighbourhood functions are key components of local-search algorithms such as Simulated Annealing or Threshold Accepting. These functions take a solution and return a slightly-modified copy of it, i.e. a neighbour. The package provides a function neighbourfun() that constructs such neighbourhood functions, based on parameters such as admissible ranges for elements in a solution. Supported are numeric and logical solutions. The algorithms were originally created for portfolio-optimisation applications, but can be used for other models as well. Several recipes for neighbour computations are taken from "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658).

Authors:Enrico Schumann [aut, cre]

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Bug tracker:https://github.com/enricoschumann/neighbours/issues

On CRAN:

algorithmslocal-searchmachine-learningneighbourhood-functionsneighbourssimulated-annealingsubset-selectionthreshold-accepting

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Last updated 12 months agofrom:8e39b27f39. Checks:OK: 7. Indexed: yes.

Exports:compare_vectorsneighborfunneighbourfunnext_subset

Dependencies: