convex.jl-f65535da-76fb-5f13-bab9-19810c17039a
Last snapshots taken from https://github.com/UnofficialJuliaMirror/Convex.jl-f65535da-76fb-5f13-bab9-19810c17039a on 2019-11-20T05:53:07.199-05:00 by @UnofficialJuliaMirrorBot via Travis job 153.10 , triggered by Travis cron job on branch "master"
https://github.com/unofficialjuliamirrorsnapshots/convex.jl-f65535da-76fb-5f13-bab9-19810c17039a
Science Score: 18.0%
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✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
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○Scientific vocabulary similarity
Low similarity (16.4%) to scientific vocabulary
Repository
Last snapshots taken from https://github.com/UnofficialJuliaMirror/Convex.jl-f65535da-76fb-5f13-bab9-19810c17039a on 2019-11-20T05:53:07.199-05:00 by @UnofficialJuliaMirrorBot via Travis job 153.10 , triggered by Travis cron job on branch "master"
Basic Info
- Host: GitHub
- Owner: UnofficialJuliaMirrorSnapshots
- License: other
- Language: Julia
- Default Branch: master
- Size: 1.48 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- 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. All examples can be downloaded as a zip file from here.
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
- Name: Unofficial Julia Mirror [Snapshots]
- Login: UnofficialJuliaMirrorSnapshots
- Kind: organization
- Website: https://github.com/UnofficialJuliaMirrorSnapshots/RepoSnapshots.jl
- Repositories: 4
- Profile: https://github.com/UnofficialJuliaMirrorSnapshots
Snapshots of all registered Julia packages. Updated weekly by @UnofficialJuliaMirrorBot. See also: @UnofficialJuliaMirror.
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}
}