Skip to main content

tvopt: A Python Framework for Time-Varying Optimization

Project description

banner

Welcome to tvopt Documentation Status

Docs | Installation | Cite

tvopt is a prototyping and benchmarking Python framework for time-varying (or online) optimization. The framework is modular, and provides different tools for modeling dynamic optimization problems and to solve them with a wide range of well known algorithms.

The documentation is available here.

Installation

tvopt works on Python 3.7 and depends on: numpy, scipy.

pip installation

pip install tvopt

Cite

@article{bastianello_tvopt_2020,
	title = {tvopt: {A} {Python} {Framework} for {Time}-{Varying} {Optimization}},
	url = {http://arxiv.org/abs/2011.07119},
	journal = {arXiv:2011.07119 [cs, math]},
	author = {Bastianello, Nicola},
	year = {2020}
}

Author

tvopt is developed by Nicola Bastianello

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

tvopt-0.1.7.tar.gz (34.7 kB view details)

Uploaded Source

File details

Details for the file tvopt-0.1.7.tar.gz.

File metadata

  • Download URL: tvopt-0.1.7.tar.gz
  • Upload date:
  • Size: 34.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/50.3.2 requests-toolbelt/0.9.1 tqdm/4.56.0 CPython/3.7.9

File hashes

Hashes for tvopt-0.1.7.tar.gz
Algorithm Hash digest
SHA256 0e5cb6398a11efe902ffbcc68d51a0990945e57d9ef909f57ba8774520b35a02
MD5 8234f52effc1044b32180f11c065c64d
BLAKE2b-256 e1a1a7e5a14aeef61695a36d7c4e3a8aef983a67e5ad1544a6662f378b48476d

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