mpcc.jl
Data Structures for Optimization Models with Complementarity Constraints
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Repository
Data Structures for Optimization Models with Complementarity Constraints
Basic Info
- Host: GitHub
- Owner: tmigot
- License: mit
- Language: Julia
- Default Branch: main
- Size: 582 KB
Statistics
- Stars: 2
- Watchers: 1
- Forks: 2
- Open Issues: 11
- Releases: 0
Metadata Files
README.md
MPCC
Set of tools to model nonlinear continuous optimization model
math
\min f(x) \text{ s.t. } l ≤ c(x) ≤ u,
extended with complementarity constraints:
math
G(x) ≥ 0, H(x) ≥ 0, G(x) ∘ H(x) ≤ 0.
The resulting model is called an MPCC.
This package also handles vanishing constraints (MPVC)
math
H(x) ≥ 0, G(x) ∘ H(x) ≤ 0,
kink constraints
math
H(x) ≥ 0, G(x) ∘ H(x) = 0,
and switching constraints
math
G(x) ∘ H(x) = 0.
Denote ∘ the componentwise product of two vectors.
This package extends the NLPModel API defined in NLPModels.jl. The role of NLPModels.jl is to define an API that users and solvers can rely on.
The package contains the basic tools to use the Stopping framework, and exports an MPCCStopping.
Example
The simplest way to define an MPCCModel uses automatic differentiation:
julia
using MPCC
f(x) = sum(x) # objective function
x0 = ones(6)
G(x) = [x[1]; x[3]]
H(x) = [x[2]; x[4]]
lccg, lcch = zeros(2), zeros(2)
lvar, uvar = fill(-10.0, size(x0)), fill(10.0, size(x0))
admpcc = ADMPCCModel(G, H, lccg, lcch, f, x0, lvar, uvar)
The resulting is an instance of an AbstractMPCCModel with all capabilities of an AbstractNLPModel such as
julia
x = rand(6)
grad(admpcc, x) # returns the gradient of the objective function
neval_grad(admpcc) # returns the number of evaluation of grad
reset!(admpcc) # reset the internal counter
but also exports functions to deal with complementarity constraints
julia
x = rand(6)
(consG(admpcc, x), consH(admpcc, x))
but also jac_G_structure, jac_H_structure, jac_G_coord, jac_H_coord, jGprod, jHprod, jGtprod, jHtprod, hGprod, hHprod.
It also possible to convert the problem as a classical nonlinear optimization model treating the complementarity constraint as a nonlinear inequalities
math
H(x) ≥ 0, G(x) ≥ 0, G(x) ∘ H(x) ≤ 0,
using NLMPCC as follows
julia
nlp = NLMPCC(admpcc)
so that
julia
cons(nlp, x)
returns a vector consisting of [c(x), G(x), H(x), G(x) ∘ H(x)].
How to Cite
If you use MPCC.jl in your work, please cite using the reference given in CITATION.cff.
Contributing
If you want to make contributions of any kind, please first that a look into our contributing guide directly on GitHub or the contributing page on the website.
Contributors
 Tangi Migot 📆 🚧 📖 ⚠️ 💻 |
Owner
- Login: tmigot
- Kind: user
- Location: Montréal
- Website: tmigot.github.io
- Repositories: 15
- Profile: https://github.com/tmigot
Citation (CITATION.cff)
# Go to https://citation-file-format.github.io/cff-initializer-javascript/#/ to finish this
cff-version: 1.2.0
title: MPCC.jl
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Tangi Migot
email: tangi.migot@gmail.com
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