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# 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 MathOptInterface. It also supports optimization with complex variables and coefficients.

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

## Quick Example

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

``````using Pkg
``````

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

``````# 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, SCS.Optimizer())

# 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.

03/08/2014

3 days ago

1130 commits