DiffOpt.jl is a package for differentiating convex optimization programs with respect to the program parameters. DiffOpt currently supports linear, quadratic, and conic programs.
DiffOpt.jl is licensed under the
MIT License.
Install DiffOpt using Pkg.add:
import Pkg
Pkg.add("DiffOpt")The documentation for DiffOpt.jl includes a detailed description of the theory behind the package, along with examples, tutorials, and an API reference.
Use DiffOpt with JuMP by following this brief example:
using JuMP, DiffOpt, HiGHS
# Create a model using the wrapper
model = Model(() -> DiffOpt.diff_optimizer(HiGHS.Optimizer))
# Define your model and solve it
@variable(model, x)
@constraint(model, cons, x >= 3)
@objective(model, Min, 2x)
optimize!(model)
# Choose the problem parameters to differentiate with respect to, and set their
# perturbations.
MOI.set(model, DiffOpt.ReverseVariablePrimal(), x, 1.0)
# Differentiate the model
DiffOpt.reverse_differentiate!(model)
# fetch the gradients
grad_exp = MOI.get(model, DiffOpt.ReverseConstraintFunction(), cons) # -3 x - 1
constant(grad_exp) # -1
coefficient(grad_exp, x) # -3DiffOpt began as a NumFOCUS sponsored Google Summer of Code (2020) project