Nonconvex.jl Documentation

Nonconvex.jl is a Julia package that implements and wraps a number of constrained nonlinear and mixed integer nonlinear programming solvers. There are 3 focus points of Nonconvex.jl compared to similar packages such as JuMP.jl and NLPModels.jl:

1. Emphasis on a function-based API. Objectives and constraints are normal Julia functions.
2. The ability to nest algorithms to create more complicated algorithms.
3. The ability to automatically handle structs and different container types in the decision variables by automatically vectorizing and un-vectorizing them in an AD compatible way.

Installing Nonconvex

To install Nonconvex.jl, open a Julia REPL and type ] to enter the package mode. Then run:

add Nonconvex

Alternatively, copy and paste the following code to a Julia REPL:

using Pkg; Pkg.add("Nonconvex")

Loading Nonconvex

To load and start using Nonconvex.jl, run:

using Nonconvex

Quick start

using Nonconvex
Nonconvex.@load NLopt

f(x) = sqrt(x[2])
g(x, a, b) = (a*x[1] + b)^3 - x[2]

model = Model(f)
addvar!(model, [0.0, 0.0], [10.0, 10.0])
add_ineq_constraint!(model, x -> g(x, 2, 0))
add_ineq_constraint!(model, x -> g(x, -1, 1))

alg = NLoptAlg(:LD_MMA)
options = NLoptOptions()
r = optimize(model, alg, [1.0, 1.0], options = options)
r.minimum # objective value
r.minimizer # decision variables

Table of contents

• Problem definition
• • Table of contents
• Algorithms
• • Overview of algorithms
  • Wrapper packages
• Gradients, Jacobians and Hessians
• • Table of contents
• Optimization result