03/08/2014

8 days ago

972 commits

**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.

To run this example, first install Convex and at least one solver, such as SCS:

```
Pkg.add("Convex")
Pkg.add("SCS")
```

Now let's solve a least-squares problem with inequality constraints.

```
# Let us first make the Convex.jl module available
using Convex
# 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)
# Check the status of the problem
problem.status # :Optimal, :Infeasible, :Unbounded etc.
# Get the optimal value
problem.optval
```

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.

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.

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