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-0.1.6.tar.gz (34.7 kB view details)

Uploaded Source

File details

Details for the file tvopt-0.1.6.tar.gz.

File metadata

  • Download URL: tvopt-0.1.6.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-0.1.6.tar.gz
Algorithm Hash digest
SHA256 99e6ca125d31b43d8863ee0596aa49967151afda1d8d153c8414fac673c6c1bd
MD5 0bf2e488c71d81260b5ada46ba9be8f2
BLAKE2b-256 7786aa004b3b871bfbf25b681a38d0d1a04d7123ccf3fa167dc0cd8e5ab6e0b0

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