⚠️ Warning This package was archived. Consider using DWave.jl instead.
D-Wave Neal Simulated Annealing Interface for JuMP
julia> import Pkg; Pkg.add("DWaveNeal")
julia> using DWaveNeal
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)
optimize!(model)
for i = 1:result_count(model)
xi = value.(model[:x]; result = i)
yi = objective_value(model; result = i)
println("[$i] f($(xi)) = $(yi)")
end
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.