D-Wave Neal Simulated Annealing Interface for JuMP


julia> import Pkg; Pkg.add("DWaveNeal")

julia> using DWaveNeal

Getting started

using JuMP
using DWaveNeal

model = Model(DWaveNeal.Optimizer)

n = 3
Q = [ -1  2  2
       2 -1  2
       2  2 -1 ]

@variable(model, x[1:n], Bin)
@objective(model, Min, x' * Q * x)


for i = 1:result_count(model)
    xi = value.(model[:x]; result = i)
    yi = objective_value(model; result = i)

    println("[$i] f($(xi)) = $(yi)")

Note: The D-Wave Neal wrapper for Julia is not officially supported by D-Wave Systems. If you are a commercial customer interested in official support for Julia from DWave, let them know!

Note: If you are using DWaveNeal.jl in your project, we recommend you to include the .CondaPkg entry in your .gitignore file. The PythonCall module will place a lot of files in this folder when building its Python environment.