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FletcherPenaltySolver

Fletcher's penalty method for nonlinear optimization models

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

Science Score: 67.0%

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    Low similarity (9.3%) to scientific vocabulary
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Repository

Fletcher's penalty method for nonlinear optimization models

Basic Info
  • Host: GitHub
  • Owner: JuliaSmoothOptimizers
  • License: other
  • Language: Julia
  • Default Branch: main
  • Homepage:
  • Size: 3.29 MB
Statistics
  • Stars: 9
  • Watchers: 2
  • Forks: 0
  • Open Issues: 14
  • Releases: 13
Created over 5 years ago · Last pushed 11 months ago
Metadata Files
Readme License Citation

README.md

FletcherPenaltySolver.jl - Fletcher's Penalty Method

docs-stable docs-dev build-ci codecov release doi

FPS is a solver for equality-constrained nonlinear problems, i.e., optimization problems of the form

min f(x)     s.t.     c(x) = 0.

It uses other JuliaSmoothOptimizers packages for development. In particular, NLPModels.jl is used for defining the problem, and SolverCore.jl for the output. If a general inequality-constrained problem is given to the solver, it solves the problem reformulated as a SlackModel from NLPModelsModifiers.jl.

Algorithm

For equality-constrained problems, the method iteratively solves an unconstrained problem. For bound and equality-constrained problems, the subproblems are bound-constrained problems. Any solver compatible with Stopping.jl can be used. By default, we use ipopt from NLPModelsIpopt.jl to solve the subproblem, but other solvers can be used such as knitro from NLPModelsKnitro.jl or any solvers from JSOSolvers.jl. The Stopping version of these solvers is available in StoppingInterface.jl.

It uses LDLFactorizations.jl by default to evaluate the derivatives of the penalized subproblem, but one can also use a matrix-free version with Krylov.jl.

References

Estrin, R., Friedlander, M. P., Orban, D., & Saunders, M. A. (2020). Implementing a smooth exact penalty function for equality-constrained nonlinear optimization. SIAM Journal on Scientific Computing, 42(3), A1809-A1835. 10.1137/19M1238265

How to Cite

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

Installation

pkg> add FletcherPenaltySolver

Example

```julia using FletcherPenaltySolver, ADNLPModels

Rosenbrock

nlp = ADNLPModel(x -> 100 * (x[2] - x[1]^2)^2 + (x[1] - 1)^2, [-1.2; 1.0]) stats = fps_solve(nlp)

Constrained

nlp = ADNLPModel( x -> 100 * (x[2] - x[1]^2)^2 + (x[1] - 1)^2, [-1.2; 1.0], x->[x[1] * x[2] - 1], [0.0],[1.0], ) stats = fps_solve(nlp) ```

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

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!

cff-version: 1.2.0
title: 'FletcherPenaltySolver.jl: Fletcher''s penalty method for nonlinear optimization models'
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Tangi
    family-names: Migot
    email: tangi.migot@gmail.com
    orcid: 'https://orcid.org/0000-0001-7729-2513'
  - given-names: Dominique
    family-names: Orban
    orcid: 'https://orcid.org/0000-0002-8017-7687'
  - given-names: Abel
    family-names: Soares Siqueira
    orcid: 'https://orcid.org/0000-0003-4451-281X'
  - given-names: contributors
identifiers:
  - description: Zenodo archive
    type: doi
    value: 10.5281/zenodo.7153564
keywords:
  - Nonlinear Optimization
  - Julia
  - Nonlinear Programming
license: MPL-2.0
version: 0.2.4
date-released: '2023-07-19'
repository-code: >-
  https://github.com/JuliaSmoothOptimizers/FletcherPenaltySolver.jl

GitHub Events

Total
  • Create event: 8
  • Commit comment event: 2
  • Release event: 3
  • Delete event: 6
  • Issue comment event: 3
  • Push event: 30
  • Pull request event: 12
Last Year
  • Create event: 8
  • Commit comment event: 2
  • Release event: 3
  • Delete event: 6
  • Issue comment event: 3
  • Push event: 30
  • Pull request event: 12

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 17
  • Total pull requests: 100
  • Average time to close issues: 4 months
  • Average time to close pull requests: 10 days
  • Total issue authors: 3
  • Total pull request authors: 2
  • Average comments per issue: 0.88
  • Average comments per pull request: 0.79
  • Merged pull requests: 93
  • Bot issues: 0
  • Bot pull requests: 24
Past Year
  • Issues: 1
  • Pull requests: 8
  • Average time to close issues: N/A
  • Average time to close pull requests: about 8 hours
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.13
  • Merged pull requests: 8
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • tmigot (17)
  • dpo (1)
  • JuliaTagBot (1)
Pull Request Authors
  • tmigot (86)
  • github-actions[bot] (25)
Top Labels
Issue Labels
documentation (1)
Pull Request Labels
formatting (18) automated pr (18) no changelog (18) documentation (3) enhancement (2) bug (1)

Packages

  • Total packages: 1
  • Total downloads:
    • julia 2 total
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 13
juliahub.com: FletcherPenaltySolver

Fletcher's penalty method for nonlinear optimization models

  • Versions: 13
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 2 Total
Rankings
Dependent repos count: 9.9%
Average: 34.5%
Stargazers count: 35.6%
Dependent packages count: 38.9%
Forks count: 53.5%
Last synced: 11 months ago

Dependencies

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