DiffOpt.jl

Differentiating convex optimization programs w.r.t. program parameters
Popularity
109 Stars
Updated Last
12 Months Ago
Started In
May 2020

DiffOpt.jl

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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)  # -3

GSOC2020

DiffOpt began as a NumFOCUS sponsored Google Summer of Code (2020) project

Used By Packages

No packages found.