Convex.jl is a Julia package for Disciplined Convex Programming (DCP).
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.
Convex.jl also supports optimization with complex variables and coefficients.
For usage questions, please contact us via Discourse.
If you have a reproducible example of a bug, please open a GitHub issue.
Install Convex using the Julia package manager:
import Pkg
Pkg.add("Convex")# 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 0 <= x <= 1
# This can be done by: minimize(objective, constraints)
problem = minimize(sumsquares(A * x - b), [x >= 0, x <= 1])
# Solve the problem by calling solve!
solve!(problem, SCS.Optimizer)
# Check the status of the problem
problem.status
# Get the optimal value
problem.optvalConvex.jl contains an experimental JuMP solver. This solver reformulates a
nonlinear JuMP model into a conic program using DCP. Note that it currently
supports only a limited subset of scalar nonlinear programs, such as those
involving log and exp.
julia> using JuMP, Convex, Clarabel
julia> model = Model(() -> Convex.Optimizer(Clarabel.Optimizer));
julia> set_silent(model)
julia> @variable(model, x >= 1);
julia> @variable(model, t);
julia> @constraint(model, t >= exp(x))
t - exp(x) ≥ 0
julia> @objective(model, Min, t);
julia> optimize!(model)
julia> value(x), value(t)
(0.9999999919393833, 2.7182818073461403)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",
}