DiffOpt.jl
DiffOpt.jl is a package for differentiating convex optimization programs with respect to the program parameters. DiffOpt currently supports linear, quadratic, and conic programs.
License
DiffOpt.jl is licensed under the
MIT License.
Installation
Install DiffOpt using Pkg.add:
import Pkg
Pkg.add("DiffOpt")Documentation
The documentation for DiffOpt.jl includes a detailed description of the theory behind the package, along with examples, tutorials, and an API reference.
Use with JuMP
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) # -3GSOC2020
DiffOpt began as a NumFOCUS sponsored Google Summer of Code (2020) project