sdpa-python
SemiDefinite Programming Algorithm (SDPA) for Python
Science Score: 31.0%
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Low similarity (13.4%) to scientific vocabulary
Keywords
Repository
SemiDefinite Programming Algorithm (SDPA) for Python
Basic Info
- Host: GitHub
- Owner: sdpa-python
- License: gpl-2.0
- Language: C++
- Default Branch: main
- Homepage: http://sdpa-python.github.io
- Size: 508 KB
Statistics
- Stars: 12
- Watchers: 1
- Forks: 2
- Open Issues: 1
- Releases: 0
Topics
Metadata Files
README.md
SDPA for Python
SDPA for Python is a Python 3 wrapper for SDPA (SemiDefinite Programming Algorithm). SDPA is a software package for solving general SDPs based on primal-dual interior-point methods with the HRVW/KSH/M search direction [1].
This package is a fork of SDPAP, the Python interface for SDPA provided at the official SDPA website. This repository aims to provide Python 3 support for both SDPA and SDPA Multiprecision (fork of SDPA-GMP [4]).
Two variants of this package are available on the Python Package Index (PyPI). The package using the SDPA (OpenBLAS) backend can be installed by
bash
pip install sdpa-python
The package using the SDPA Multiprecision (GMP) backend can be installed by
bash
pip install sdpa-multiprecision
For usage documentation or to build from source, please see the documentation website.
History
SDPA was officially developed between 1995 and 2012 by Makoto Yamashita, Katsuki Fujisawa, Masakazu Kojima, Mituhiro Fukuda, Kazuhiro Kobayashi, Kazuhide Nakata, Maho Nakata and Kazushige Goto [1] [2] [3]. The official SDPA website contains an unmaintained version of SDPA.
SDPAP was written by Kenta Kato as a Python 2 interface for SDPA. The official SDPA website also contains an unmaintained version of SDPAP.
This package is a Python 3 port of SDPAP. Besides Python 3 support, it also adds support for the multiprecision backend.
References
If you are using SDPA for Python in your research, please cite SDPA by citing the following papers and book chapters. The BibTex of the below has been included in the repository.
[1] Makoto Yamashita, Katsuki Fujisawa and Masakazu Kojima, "Implementation and evaluation of SDPA 6.0 (Semidefinite Programming Algorithm 6.0)," Optimization Methods and Software, vol. 18, no. 4, pp. 491–505, 2003, doi: 10.1080/1055678031000118482.
[2] Makoto Yamashita, Katsuki Fujisawa, Kazuhide Nakata, Maho Nakata, Mituhiro Fukuda, Kazuhiro Kobayashi, and Kazushige Goto, "A high-performance software package for semidefinite programs: SDPA 7," Research Report B-460 Dept. of Mathematical and Computing Science, Tokyo Institute of Technology, Tokyo, Japan, September, 2010.
[3] Makoto Yamashita, Katsuki Fujisawa, Mituhiro Fukuda, Kazuhiro Kobayashi, Kazuhide Nakata and Maho Nakata, “Latest Developments in the SDPA Family for Solving Large-Scale SDPs,” in Handbook on Semidefinite, Conic and Polynomial Optimization, M. F. Anjos and J. B. Lasserre, Eds. Boston, MA: Springer US, 2012, pp. 687–713. doi: 10.1007/978-1-4614-0769-0_24.
[4] Nakata, M. (2010). A numerical evaluation of highly accurate multiple-precision arithmetic version of semidefinite programming solver: SDPA-GMP, -QD and -DD. 2010 IEEE International Symposium on Computer-Aided Control System Design, 29–34. doi: 10.1109/CACSD.2010.5612693
Owner
- Name: sdpa-python
- Login: sdpa-python
- Kind: organization
- Repositories: 2
- Profile: https://github.com/sdpa-python
Citation (CITATIONS.bib)
@article{doi:10.1080/1055678031000118482,
author = "Yamashita, Makoto
and Fujisawa, Katsuki
and Kojima, Masakazu",
title = "Implementation and evaluation of SDPA 6.0 (Semidefinite Programming Algorithm 6.0)",
journal = "Optimization Methods and Software",
volume = "18",
number = "4",
pages = "491-505",
year = "2003",
publisher = "Taylor & Francis",
doi = "10.1080/1055678031000118482",
URL = "https://doi.org/10.1080/1055678031000118482",
eprint = "https://doi.org/10.1080/1055678031000118482"
}
@Inbook{Yamashita2012,
author = "Yamashita, Makoto
and Fujisawa, Katsuki
and Fukuda, Mituhiro
and Kobayashi, Kazuhiro
and Nakata, Kazuhide
and Nakata, Maho",
editor = "Anjos, Miguel F.
and Lasserre, Jean B.",
title = "Latest Developments in the SDPA Family for Solving Large-Scale SDPs",
bookTitle = "Handbook on Semidefinite, Conic and Polynomial Optimization",
year = "2012",
publisher = "Springer US",
address = "Boston, MA",
pages = "687--713",
isbn = "978-1-4614-0769-0",
doi = "10.1007/978-1-4614-0769-0_24",
url = "https://doi.org/10.1007/978-1-4614-0769-0_24"
}
@inproceedings{doi:10.1109/CACSD.2010.5612693,
author = "Nakata, Maho",
booktitle = "2010 IEEE International Symposium on Computer-Aided Control System Design",
title = "A numerical evaluation of highly accurate multiple-precision arithmetic version of semidefinite programming solver: SDPA-GMP, -QD and -DD.",
year = "2010",
volume = "",
number = "",
pages = "29-34",
doi = "10.1109/CACSD.2010.5612693"
}
@article{Kim2011,
author = "Kim, Sunyoung
and Kojima, Masakazu
and Mevissen, Martin
and Yamashita, Makoto",
title = "Exploiting sparsity in linear and nonlinear matrix inequalities via positive semidefinite matrix completion",
journal = "Mathematical Programming",
year = "2011",
month = "Sep",
day = "01",
volume = "129",
number = "1",
pages = "33-68",
issn = "1436-4646",
doi = "10.1007/s10107-010-0402-6",
url = "https://doi.org/10.1007/s10107-010-0402-6"
}
GitHub Events
Total
- Create event: 1
- Release event: 1
- Issues event: 2
- Watch event: 2
- Issue comment event: 6
- Push event: 7
- Fork event: 3
Last Year
- Create event: 1
- Release event: 1
- Issues event: 2
- Watch event: 2
- Issue comment event: 6
- Push event: 7
- Fork event: 3
Packages
- Total packages: 1
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Total downloads:
- pypi 1,394 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 9
- Total maintainers: 1
pypi.org: sdpa-python
SDPA (SemiDefinite Programming Algorithm) for Python
- Homepage: https://sdpa-python.github.io
- Documentation: https://sdpa-python.readthedocs.io/
- License: GNU GPL version 2
-
Latest release: 0.2.2
published 12 months ago