Skip to main content

A set of well-selected test functions for unconstrained global optimization

Project description

This library contains the implementation of various well-selected benchmark problems required in order to examine the performance of unconstrained global optimization methods.

The latest version currently contain the following benchmark problems: -> Alpine Function -> Bartels Conn Function -> Beale Function -> Booth Function -> Deckkers-Aarts Function -> Egg Crate Function -> Goldstein Price Function -> Leon Function -> Matyas Function -> Powell Sum Function

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for global_opt, version 1.0
Filename, size File type Python version Upload date Hashes
Filename, size global_opt-1.0.1.win-amd64.exe (239.1 kB) File type Windows Installer Python version 2.7 Upload date Hashes View
Filename, size global_opt-1.0.1.win-amd64.msi (196.6 kB) File type Windows MSI Installer Python version 2.7 Upload date Hashes View
Filename, size global_opt-1.0.win-amd64.msi (180.2 kB) File type Windows MSI Installer Python version 2.7 Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page