NLPModels

Data Structures for Optimization Models

https://github.com/juliasmoothoptimizers/nlpmodels.jl

Science Score: 77.0%

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Low similarity (15.1%) to scientific vocabulary

Keywords

julia julia-language nlpmodels nonlinear-optimization nonlinear-programming optimization

Keywords from Contributors

factorization preconditioner matrices linear-operators linear-maps krylov optimization-algorithms performance-profile linear-systems cutest

Last synced: 4 months ago · JSON representation ·

Repository

Data Structures for Optimization Models

Basic Info

Host: GitHub
Owner: JuliaSmoothOptimizers
License: other
Language: Julia
Default Branch: main
Homepage:
Size: 5.13 MB

Statistics

Stars: 181
Watchers: 12
Forks: 37
Open Issues: 16
Releases: 57

Topics

julia julia-language nlpmodels nonlinear-optimization nonlinear-programming optimization

Created over 10 years ago · Last pushed 5 months ago

Metadata Files

Readme License Citation Zenodo

NLPModels

| Documentation | CI | Coverage | Release | DOI | |:-----------------:|:------:|:------------:|:-----------:|:-------:| | | | | | |

This package provides general guidelines to represent non-linear programming (NLP) problems in Julia and a standardized API to evaluate the functions and their derivatives. The main objective is to be able to rely on that API when designing optimization solvers in Julia.

How to Cite

If you use NLPModels.jl in your work, please cite using the format given in CITATION.cff.

Optimization Problems

Optimization problems are represented by an instance of (a subtype of) AbstractNLPModel. Such instances are composed of * an instance of NLPModelMeta, which provides information about the problem, including the number of variables, constraints, bounds on the variables, etc. * other data specific to the provenance of the problem.

See the documentation for details on the models and the API.

Installation

julia pkg> add NLPModels

Models

This package provides no models, although it allows the definition of manually written models.

Check the list of packages that define models in this page of the docs

Main Methods

If model is an instance of an appropriate subtype of AbstractNLPModel, the following methods are normally defined:

obj(model, x): evaluate f(x), the objective at x
cons(model x): evaluate c(x), the vector of general constraints at x

The following methods are defined if first-order derivatives are available:

grad(model, x): evaluate ∇f(x), the objective gradient at x
jac(model, x): evaluate J(x), the Jacobian of c at x as a sparse matrix

If Jacobian-vector products can be computed more efficiently than by evaluating the Jacobian explicitly, the following methods may be implemented:

jprod(model, x, v): evaluate the result of the matrix-vector product J(x)⋅v
jtprod(model, x, u): evaluate the result of the matrix-vector product J(x)ᵀ⋅u

The following method is defined if second-order derivatives are available:

hess(model, x, y): evaluate ∇²L(x,y), the Hessian of the Lagrangian at x and y

If Hessian-vector products can be computed more efficiently than by evaluating the Hessian explicitly, the following method may be implemented:

hprod(model, x, v, y): evaluate the result of the matrix-vector product ∇²L(x,y)⋅v

Several in-place variants of the methods above may also be implemented.

The complete list of methods that an interface may implement can be found in the documentation.

Attributes

NLPModelMeta objects have the following attributes (with S <: AbstractVector):

Attribute | Type ------------|---- nvar x0 lvar uvar ifix ilow iupp irng ifree iinf ncon nlin nnln y0 lcon ucon lin nln jfix jlow jupp jrng jfree jinf nnzo nnzh nnzj lin_nnzj nln_nnzj minimize islp name | Notes ----------------|------------------------------------ | Int | number of variables | S | initial guess | S | vector of lower bounds | S | vector of upper bounds | Vector{Int} | indices of fixed variables | Vector{Int} | indices of variables with lower bound only | Vector{Int} | indices of variables with upper bound only | Vector{Int} | indices of variables with lower and upper bound (range) | Vector{Int} | indices of free variables | Vector{Int} | indices of visibly infeasible bounds | Int | total number of general constraints | Int | number of linear constraints | Int | number of nonlinear general constraints | S | initial Lagrange multipliers | S | vector of constraint lower bounds | S | vector of constraint upper bounds | Vector{Int} | indices of linear constraints | Vector{Int} | indices of nonlinear constraints | Vector{Int} | indices of equality constraints | Vector{Int} | indices of constraints of the form c(x) ≥ cl | Vector{Int} | indices of constraints of the form c(x) ≤ cu | Vector{Int} | indices of constraints of the form cl ≤ c(x) ≤ cu | Vector{Int} | indices of "free" constraints (there shouldn't be any) | Vector{Int} | indices of the visibly infeasible constraints | Int | number of nonzeros in the gradient | Int | number of nonzeros in the sparse Hessian | Int | number of nonzeros in the sparse Jacobian | Int | number of nonzeros in the linear part of sparse Jacobian | Int | number of nonzeros in the nonlinear part of sparse Jacobian | Bool | true if optimize == minimize | Bool | true if the problem is a linear program | String | problem name

Bug reports and discussions

If you think you found a bug, feel free to open an issue. Focused suggestions and requests can also be opened as issues. Before opening a pull request, start an issue or a discussion on the topic, please.

If you want to ask a question not suited for a bug report, feel free to start a discussion here. This forum is for general discussion about this repository and the JuliaSmoothOptimizers, so questions about any of our packages are welcome.

Owner

Name: JuliaSmoothOptimizers
Login: JuliaSmoothOptimizers
Kind: organization
Location: DOI: 10.5281/zenodo.2655082

Website: https://juliasmoothoptimizers.github.io
Repositories: 63
Profile: https://github.com/JuliaSmoothOptimizers

Infrastructure and Solvers for Continuous Optimization in Julia

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

authors:
  - affiliation: >-
      GERAD and Department of Mathematics and
      Industrial Engineering, Polytechnique Montréal,
      QC, Canada
    family-names: Orban
    given-names: Dominique
    orcid: 'https://orcid.org/0000-0002-8017-7687'
    email: dominique.orban@gerad.ca
  - affiliation: 'Netherlands eScience Center, Amsterdam, NL'
    family-names: Soares Siqueira
    given-names: Abel
    orcid: 'https://orcid.org/0000-0003-4451-281X'
    email: abel.s.siqueira@gmail.com
  - family-names: contributors
cff-version: 1.2.0
identifiers:
  - description: Zenodo archive
    type: doi
    value: 10.5281/zenodo.2558627
keywords:
  - Nonlinear Optimization
  - Julia
  - Nonlinear Programming
license: MPL-2.0
message: >-
  If you use this software, please cite it using the
  metadata from this file.
repository-code: >-
  https://github.com/JuliaSmoothOptimizers/NLPModels.jl
title: >-
  NLPModels.jl: Data Structures for Optimization Models
type: software
version: 0.20.0
date-released: '2023-03-23'

GitHub Events

Total

Create event: 17
Commit comment event: 4
Release event: 2
Issues event: 11
Watch event: 8
Delete event: 10
Issue comment event: 51
Push event: 60
Pull request review comment event: 30
Pull request review event: 36
Pull request event: 32
Fork event: 2

Last Year

Create event: 17
Commit comment event: 4
Release event: 2
Issues event: 11
Watch event: 8
Delete event: 10
Issue comment event: 51
Push event: 60
Pull request review comment event: 30
Pull request review event: 36
Pull request event: 32
Fork event: 2

Committers

Last synced: 7 months ago

All Time

Total Commits: 622
Total Committers: 20
Avg Commits per committer: 31.1
Development Distribution Score (DDS): 0.477

Past Year

Commits: 30
Committers: 6
Avg Commits per committer: 5.0
Development Distribution Score (DDS): 0.6

Top Committers

Name	Email	Commits
Abel Soares Siqueira	a**a@g**m	325
Dominique Orban	d**n@g**m	153
tmigot	t**t@g**m	62
Alexis Montoison	a**n@p**a	35
Geoffroy Leconte	g**0@g**m	12
AntoninKns	a**s@e**r	7
github-actions[bot]	4****]	6
Páll Haraldsson	P**n@g**m	5
RAYNAUD Paul (raynaudp)	r**p@g**n	2
MaxenceGollier	1****r	2
Monssaf Toukal	t**f@g**m	2
Tamas K. Papp	t**p@g**m	2
probot-auto-merge[bot]	p****]	2
Alberto De Marchi	a**i@g**m	1
Elliot Saba	s**t@g**m	1
JPD	v**i@g**m	1
Julia TagBot	5****t	1
Rishabh Gupta	6****7	1
Xiukun Hu	x**4@u**u	1
Tim Holy	t**y@g**m	1

Committer Domains (Top 20 + Academic)

uwyo.edu: 1 grenoble-inp.lan: 1 etu.enseeiht.fr: 1 polymtl.ca: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 44
Total pull requests: 133
Average time to close issues: 6 months
Average time to close pull requests: 18 days
Total issue authors: 17
Total pull request authors: 16
Average comments per issue: 3.61
Average comments per pull request: 3.04
Merged pull requests: 109
Bot issues: 0
Bot pull requests: 16

Past Year

Issues: 7
Pull requests: 27
Average time to close issues: 2 days
Average time to close pull requests: 3 days
Issue authors: 2
Pull request authors: 4
Average comments per issue: 1.43
Average comments per pull request: 1.44
Merged pull requests: 18
Bot issues: 0
Bot pull requests: 2

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Top Authors

Issue Authors

tmigot (15)
amontoison (7)
abelsiqueira (5)
dpo (3)
vepiteski (2)
frapac (2)
danphenderson (1)
votroto (1)
finmod (1)
AntoninKns (1)
MaxenceGollier (1)
tnnguyen103 (1)
JuliaTagBot (1)
geoffroyleconte (1)
sshin23 (1)

Pull Request Authors

tmigot (63)
amontoison (27)
github-actions[bot] (20)
abelsiqueira (14)
geoffroyleconte (7)
arnavk23 (6)
MaxenceGollier (4)
dpo (3)
timholy (2)
paraynaud (2)
JSOBot (2)
grishabh147 (1)
frapac (1)
aldma (1)
PallHaraldsson (1)

Top Labels

Issue Labels

enhancement (9) documentation (6) bug (4) Good first issue (3) up for grabs (1) question (1)

Pull Request Labels

formatting (16) automated pr (16) no changelog (16) documentation (7) CI (2) enhancement (2) ready to go (1)

Packages

Total packages: 1
Total downloads:
- julia 614 total

Total dependent packages: 40
Total dependent repositories: 8
Total versions: 48

juliahub.com: NLPModels

Data Structures for Optimization Models

Documentation: https://docs.juliahub.com/General/NLPModels/stable/
License: MPL-2.0
Latest release: 0.21.5
published 8 months ago

Versions: 48
Dependent Packages: 40
Dependent Repositories: 8
Downloads: 614 Total

Rankings

Dependent packages count: 1.9%

Dependent repos count: 3.7%

Average: 3.9%

Forks count: 5.0%

Stargazers count: 5.2%

Last synced: 4 months ago

Dependencies

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NLPModels

Science Score: 77.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

NLPModels

How to Cite

Optimization Problems

Installation

Models

Main Methods

Attributes

Bug reports and discussions

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

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Packages

juliahub.com: NLPModels

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