A JuMP-based Nonlinear Integer Program Solver
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README.md

Juniper

Release: Juniper Juniper Docs

Dev: Build Status codecov Docs

Idea

You have a non linear problem with discrete variables (MINLP) and want some more control over the branch and bound part. The relaxation should be solveable by any solver you prefer. Some solvers might not be able to solve the mixed integer part by themselves.

Juniper (Jump Nonlinear Integer Program solver) is a heuristic for non convex problems. You need the global optimum? Check out POD.jl

Basic usage

Juniper can be installed via:

Pkg.add("Juniper")

or ] add Juniper for Julia v0.7 and v1.

Then adding it to your project by

using Juniper

You also have to import your NLP solver i.e.

using Ipopt

as well as JuMP

Define JuniperSolver as your solver:

solver = JuniperSolver(IpoptSolver(print_level=0))

And give it a go:

m = Model(solver=solver)

v = [10,20,12,23,42]
w = [12,45,12,22,21]
@variable(m, x[1:5], Bin)

@objective(m, Max, dot(v,x))

@NLconstraint(m, sum(w[i]*x[i]^2 for i=1:5) <= 45)   

status = solve(m)

This solver is a NLP solver therefore you should have at least one NLconstraint or NLobjective.

It is recommended to specify a mip solver as well i.e.

using Cbc
solver = JuniperSolver(IpoptSolver(print_level=0);   
                       mip_solver=CbcSolver())

Then the feasibility pump is used to find a feasible solution before the branch and bound part starts. This turned out to be highly effective.

Citing Juniper

If you find Juniper useful in your work, we kindly request that you cite the following paper or technical report:

@inproceedings{juniper,
     Author = {Ole Kröger and Carleton Coffrin and Hassan Hijazi and Harsha Nagarajan},
     Title = {Juniper: An Open-Source Nonlinear Branch-and-Bound Solver in Julia},
     booktitle="Integration of Constraint Programming, Artificial Intelligence, and Operations Research",
     pages="377--386",
     year="2018",
     publisher="Springer International Publishing",
     isbn="978-3-319-93031-2"
}