convex.jl
Science Score: 41.0%
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Repository
Basic Info
- Host: GitHub
- Owner: milesfrain
- License: other
- Default Branch: master
- Size: 2.85 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 3
- Releases: 0
Metadata Files
README.md
Convex.jl
Convex.jl is a Julia package for Disciplined Convex Programming. Convex.jl can solve linear programs, mixed-integer linear programs, and DCP-compliant convex programs using a variety of solvers, including Mosek, Gurobi, ECOS, SCS, and GLPK, through the MathProgBase interface. It also supports optimization with complex variables and coefficients.
Installation: julia> Pkg.add("Convex")
- Detailed documentation and examples for Convex.jl (stable | latest).
- If you're running into bugs or have feature requests, please use the Github Issue Tracker.
- For usage questions, please contact us via Discourse.
Quick Example
To run this example, first install Convex and at least one solver, such as SCS:
julia
using Pkg
Pkg.add("Convex")
Pkg.add("SCS")
Now let's solve a least-squares problem with inequality constraints.
```julia
Let us first make the Convex.jl module available
using Convex, SCS
Generate random problem data
m = 4; n = 5 A = randn(m, n); b = randn(m, 1)
Create a (column vector) variable of size n x 1.
x = Variable(n)
The problem is to minimize ||Ax - b||^2 subject to x >= 0
This can be done by: minimize(objective, constraints)
problem = minimize(sumsquares(A * x - b), [x >= 0])
Solve the problem by calling solve!
solve!(problem, SCSSolver())
Check the status of the problem
problem.status # :Optimal, :Infeasible, :Unbounded etc.
Get the optimal value
problem.optval ```
More Examples
A number of examples can be found here. The basic usage notebook gives a simple tutorial on problems that can be solved using Convex.jl. Many use cases of the package in complex-domain optimization can be found here.
Note: many of the examples are currently outdated (August 2019). Pull requests to update them are greatly appreciated!
Citing this package
If you use Convex.jl for published work, we encourage you to cite the software using the following BibTeX citation:
@article{convexjl,
title = {Convex Optimization in {J}ulia},
author ={Udell, Madeleine and Mohan, Karanveer and Zeng, David and Hong, Jenny and Diamond, Steven and Boyd, Stephen},
year = {2014},
journal = {SC14 Workshop on High Performance Technical Computing in Dynamic Languages},
archivePrefix = "arXiv",
eprint = {1410.4821},
primaryClass = "math-oc",
}
Convex.jl was previously called CVX.jl.
Owner
- Login: milesfrain
- Kind: user
- Repositories: 7
- Profile: https://github.com/milesfrain
Citation (CITATION.bib)
@inproceedings{Convex.jl-2014,
title={Convex optimization in {J}ulia},
author={Udell, Madeleine and Mohan, Karanveer and Zeng, David and Hong, Jenny and Diamond, Steven and Boyd, Stephen},
booktitle={Proceedings of the 1st First Workshop for High Performance Technical Computing in Dynamic Languages},
pages={18--28},
year={2014},
organization={IEEE Press}
}
GitHub Events
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Top Committers
| Name | Commits | |
|---|---|---|
| Ayush Pandey | a****p@g****m | 266 |
| Madeleine Udell | m****l@g****m | 231 |
| Karanveer Mohan | k****2@g****m | 184 |
| David Zeng | d****g@g****m | 57 |
| Alex Arslan | a****n@c****t | 31 |
| Eric Hanson | 5****n | 31 |
| Miles Lubin | m****n@g****m | 24 |
| Jenny Hong | j****g@g****m | 21 |
| Iain Dunning | i****g@g****m | 12 |
| Karanveer Mohan | k****n@s****u | 8 |
| Mattias Fält | f****s@g****m | 4 |
| John Duchi | j****i@s****u | 4 |
| Rahul | v****l@g****m | 3 |
| hsnamkoong | h****g@g****m | 3 |
| Blake R. Johnson | b****n@b****m | 2 |
| David de Laat | m****l@d****l | 2 |
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| Nan Ge | n****1@g****m | 2 |
| Peter Wittek | p****k | 2 |
| Rory Finnegan | r****n@g****m | 2 |
| Tony Kelman | t****y@k****t | 2 |
| nkottary | k****4@g****m | 1 |
| Marcus P S | m****a@b****m | 1 |
| Raphael Saavedra | r****a@e****r | 1 |
| mbesancon | m****n@g****m | 1 |
| femtocleaner[bot] | f****] | 1 |
| Yusheng Yves Xie | y****9@g****m | 1 |
| Viral B. Shah | v****l@m****g | 1 |
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