pyOptSparse
pyOptSparse: A Python framework for large-scale constrained nonlinear optimization of sparse systems - Published in JOSS (2020)
Science Score: 98.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 4 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org -
✓Committers with academic emails
13 of 53 committers (24.5%) from academic institutions -
✓Institutional organization owner
Organization mdolab has institutional domain (mdolab.engin.umich.edu) -
✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords from Contributors
Scientific Fields
Repository
pyOptSparse is an object-oriented framework for formulating and solving nonlinear constrained optimization problems in an efficient, reusable, and portable manner.
Basic Info
- Host: GitHub
- Owner: mdolab
- License: lgpl-3.0
- Language: Python
- Default Branch: main
- Homepage: https://mdolab-pyoptsparse.readthedocs-hosted.com/en/latest/
- Size: 2.75 MB
Statistics
- Stars: 247
- Watchers: 21
- Forks: 117
- Open Issues: 36
- Releases: 51
Metadata Files
README.md
pyOptSparse is an object-oriented framework for formulating and solving nonlinear constrained optimization problems in an efficient, reusable, and portable manner. It is a fork of pyOpt that uses sparse matrices throughout the code to more efficiently handle large-scale optimization problems. Many optimization techniques can be used in pyOptSparse, including both gradient-based and gradient-free methods. A visualization tool called OptView also comes packaged with pyOptSparse, which shows the optimization history through an interactive GUI. An example output from OptView is shown below.
Optimizer support
pyOptSparse provides Python interfaces for a number of optimizers. ALPSO, CONMIN, IPOPT, NLPQLP, NSGA2, PSQP, SLSQP, ParOpt and SNOPT are currently tested and supported.
We do not provide the source code for SNOPT and NLPQLP, due to their restrictive license requirements. Please contact the authors of the respective optimizers if you wish to obtain them. Furthermore, ParOpt and IPOPT are available as open source packages but must be installed separately. Please see the documentation page of each optimizer for purchase and installation instructions.
Integration into other frameworks
pyOptSparse can be used in the following optimization frameworks: - MACH-Aero - OpenMDAO and by extension OpenAeroStruct - SUAVE
Documentation
Please see the documentation for installation details and API documentation.
Testing
Testing is done with the testflo package developed by the openMDAO team, which can be installed via pip install testflo.
To run the tests, simply type testflo . in the root directory.
Citation
If you use pyOptSparse, please see this page for citation information. A list of works that have used pyOptSparse can be found here
License
pyOptSparse is licensed under the GNU Lesser General Public License.
See LICENSE for the full license.
Copyright
Copyright (c) 2011 University of Toronto\ Copyright (c) 2014 University of Michigan\ Additional copyright (c) 2014 Gaetan K. W. Kenway, Ruben Perez, Charles A. Mader, and\ Joaquim R. R. A. Martins\ All rights reserved.
Owner
- Name: MDO Lab
- Login: mdolab
- Kind: organization
- Website: mdolab.engin.umich.edu
- Repositories: 21
- Profile: https://github.com/mdolab
Multidisciplinary Design Optimization Laboratory at the University of Michigan
JOSS Publication
pyOptSparse: A Python framework for large-scale constrained nonlinear optimization of sparse systems
Authors
Department of Aerospace Engineering, University of Michigan
Department of Aerospace Engineering, University of Michigan
Department of Aerospace Engineering, University of Michigan
Department of Aerospace Engineering, University of Michigan
Department of Aerospace Engineering, University of Michigan
Editor
Jack Poulson
Tags
optimizationGitHub Events
Total
- Create event: 15
- Release event: 4
- Issues event: 17
- Watch event: 24
- Delete event: 14
- Member event: 4
- Issue comment event: 124
- Push event: 121
- Pull request review comment event: 44
- Pull request review event: 94
- Pull request event: 46
- Fork event: 10
Last Year
- Create event: 15
- Release event: 4
- Issues event: 17
- Watch event: 24
- Delete event: 14
- Member event: 4
- Issue comment event: 124
- Push event: 121
- Pull request review comment event: 44
- Pull request review event: 94
- Pull request event: 46
- Fork event: 10
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Neil Wu | n****u@u****u | 210 |
| kenway | k****y@l****t | 155 |
| John Jasa | j****1@g****m | 59 |
| lambe | l****e@l****t | 37 |
| kmarsteller | k****r@n****v | 29 |
| Nicolas Bons | n****s@u****u | 22 |
| Ella Wu | 6****3 | 17 |
| Eirikur Jonsson | 3****j | 13 |
| Sabet Seraj | 4****j | 13 |
| Rob Falck | r****k@g****m | 13 |
| Graeme Kennedy | g****y@a****u | 13 |
| mader | m****r@l****t | 10 |
| swryan | s****n@g****m | 10 |
| Kenneth-T-Moore | k****1@n****v | 10 |
| Marco Mangano | 3****o | 10 |
| Shugo Kaneko | 4****h | 6 |
| Justin Gray | j****y@g****m | 6 |
| Bret Naylor | n****b@g****m | 6 |
| frza | f****a@d****k | 5 |
| Kenneth Moore | K****1@g****m | 4 |
| gkennedy | g****y@l****t | 4 |
| Drayton Munster | d****r@n****v | 3 |
| Joaquim R. R. A. Martins | j****m@u****u | 3 |
| Benjamin Brelje | b****e@g****m | 3 |
| Ping He | f****e@g****m | 3 |
| elee | e****e@l****t | 2 |
| Laurentww | 3****w | 2 |
| Phil Chiu | w****l | 2 |
| Eytan Adler | 6****r | 2 |
| Gaetan Kenway | g****k@g****m | 2 |
| and 23 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 74
- Total pull requests: 167
- Average time to close issues: 6 months
- Average time to close pull requests: 10 days
- Total issue authors: 35
- Total pull request authors: 23
- Average comments per issue: 1.92
- Average comments per pull request: 3.05
- Merged pull requests: 125
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 6
- Pull requests: 52
- Average time to close issues: 7 days
- Average time to close pull requests: 9 days
- Issue authors: 6
- Pull request authors: 11
- Average comments per issue: 1.67
- Average comments per pull request: 2.85
- Merged pull requests: 25
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- nwu63 (18)
- ewu63 (9)
- kanekosh (3)
- qwefhajk (2)
- Tarnarmour (2)
- kmodexc (2)
- NAnand-TUD (2)
- robfalck (2)
- eng-jamal2023 (2)
- whophil (2)
- eirikurj (2)
- A-CGray (2)
- jackm97 (2)
- swryan (2)
- Zcaic (2)
Pull Request Authors
- ewu63 (43)
- nwu63 (37)
- whophil (10)
- marcomangano (10)
- eirikurj (9)
- kanekosh (9)
- sseraj (7)
- A-CGray (6)
- gjkennedy (5)
- awccopp (4)
- swryan (4)
- eytanadler (3)
- robfalck (3)
- dingraha (2)
- jackm97 (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
- Total downloads: unknown
- Total dependent packages: 1
- Total dependent repositories: 1
- Total versions: 10
conda-forge.org: pyoptsparse
pyOptSparse is an object-oriented framework for formulating and solving nonlinear constrained optimization problems in an efficient, reusable, and portable manner. It is a fork of pyOpt that uses sparse matrices throughout the code to more efficiently handle large-scale optimization problems. Interfaces are provided for a number of optimizers -- for the conda package the optimizers ALPSO, CONMIN, IPOPT, NSGA2, PSQP, and SLSQP are supported.
- Homepage: https://mdolab-pyoptsparse.readthedocs-hosted.com/
- License: LGPL-3.0-only
-
Latest release: 2.9.1
published over 3 years ago
Rankings
Dependencies
- actions/checkout v2 composite
- conda-incubator/setup-miniconda v2 composite
- sphinx_mdolab_theme >=1.2
- mdolab-baseclasses >=1.3.1
- numpy >=1.16
- scipy >1.2
- sqlitedict >=1.6
- build
- compilers
- ipopt
- mdolab-baseclasses >=1.3.1
- meson >=1.3.2
- numpy >=1.21
- parameterized
- pip
- pkg-config
- python >=3.9
- scipy >=1.7
- setuptools
- sqlitedict >=1.6
- swig
- testflo