Skip to main content

Simulated Annealing in Python

Project description

simanneal is a python implementation of the [simulated annealing optimization](http://en.wikipedia.org/wiki/Simulated_annealing) technique.

Simulated annealing is used to find a close-to-optimal solution among an extremely large (but finite) set of potential solutions. It is particularly useful for [combinatorial optimization](http://en.wikipedia.org/wiki/Combinatorial_optimization) problems defined by complex objective functions that rely on external data.

Project details


Download files

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

Source Distribution

simanneal-0.3.0.tar.gz (4.4 kB view details)

Uploaded Source

Built Distribution

simanneal-0.3.0-py2.py3-none-any.whl (6.0 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file simanneal-0.3.0.tar.gz.

File metadata

  • Download URL: simanneal-0.3.0.tar.gz
  • Upload date:
  • Size: 4.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for simanneal-0.3.0.tar.gz
Algorithm Hash digest
SHA256 ef9f60346030b13bed4b2ca4c89e0e11566080cf3b38385e788665b75a9b9258
MD5 bf23577c93ea8383c0ea2b7a752ede4a
BLAKE2b-256 badd5d8d85907f17c99f03d9bdc6bbe72dfe24fad8e225202106985a5e5e6068

See more details on using hashes here.

File details

Details for the file simanneal-0.3.0-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for simanneal-0.3.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 e43c4c16b7956ba7b9cdeba6a501e3c0e18cd986685226c49d7dd9261ca67736
MD5 10a18916f453ca0f9135ca970e08aeef
BLAKE2b-256 63e9ad4264475fbdcd8efa7188bfbd8c9fbb3c4121119971b2e60a83606b25ca

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page