ParametricMCPs

This package provides a generic, differentiable mathematical programming layer by compiling mixed complementarity problems (MCPs) parameterized by a "runtime"-parameter vector. The resulting ParametricMCP can be solved for different parameter instantiations using solve(problem, parameters) and the solve routine is made differentiable via ChainRulesCore and ForwardDiff.

ParametricMCPs is a registered package. Thus, installation is as simple as:

] add ParametricMCPs

This package uses the proprietary PATH solver under the hood (via PATHSolver.jl). Therefore, you will need a license key to solve larger problems. However, by courtesy of Steven Dirkse, Michael Ferris, and Tudd Munson, temporary licenses are available free of charge. Please consult the documentation of PATHSolver.jl to learn about loading the license key.

Simple forward computation:

using ParametricMCPs

# setup a simple MCP which represents a QP with
# - cost: sum((z[1:2] - θ).^2)
# - constaints: z[1:2] >= 0

f(z, θ) = [2z[1:2] - z[3:4] - 2θ; z[1:2]]
lower_bounds = [-Inf, -Inf, 0, 0]
upper_bounds = [Inf, Inf, Inf, Inf]
parameter_dimension = 2
problem = ParametricMCP(f, lower_bounds, upper_bounds, parameter_dimension)

some_parameter = [1.0, 2.0]
solution = solve(problem, some_parameter)

# You can also warm-start the solver with an initial guess.
# For example, say that we want to solve the problem at a slightly perturbed parameter value, `some_other_parameter = some_parameter .+ 0.01`.
# Here, we can warm-start the solver by passing in the old solution as an intial guess.
# This is particularly handy for online optimization as in receding-horizon applications.
some_other_parameter = some_parameter .+ 0.01
other_solution = solve(problem, some_other_parameter; initial_guess = solution.z)

Since we provide differentiation rules via ChainRulesCore, the solver can be differentiated using your favourite ad-framework, e.g., Zygote:

using Zygote

function dummy_pipeline(θ)
    solution = ParametricMCPs.solve(problem, θ)
    sum(solution.z .^ 2)
end

Zygote.gradient(dummy_pipeline, some_parameter)

This package is effectively a thin wrapper around the great work of other people. Special thanks goes to the maintainers of the following packages:

• PATHSolver.jl
• ChainRulesCore.jl
• ForwardDiff.jl
• Symbolics.jl

For some specialized, closely related applications, you may want to consider the following packages (all of which also provide differentiation rules):

• TensorGames.jl solves finite N-player normal-form games.
• DifferentiableTrajectoryOptimization.jl solves parametric (single-player) trajectory optimization problems. The interface is very similar to ParametricMCPs.jl. Beyond the PATH solver, this package also supports backends like IPOPT and OSQP.