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.

Installation: julia> Pkg.add("Convex")

• Detailed documentation and examples for Convex.jl can be found here
• If you're running into bugs or have feature requests, please use the Github Issue Tracker.
• For usage questions, please contact us via the JuliaOpt mailing list

Here's a quick example of code that solves 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

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.