adaptiveregularization.jl

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

Science Score: 57.0%

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Low similarity (10.6%) to scientific vocabulary

Last synced: 10 months ago · JSON representation ·

Repository

Basic Info

Host: GitHub
Owner: JuliaSmoothOptimizers
License: mit
Language: Julia
Default Branch: main
Size: 11.9 MB

Statistics

Stars: 6
Watchers: 3
Forks: 3
Open Issues: 9
Releases: 3

Created over 9 years ago · Last pushed 10 months ago

Metadata Files

Readme License Code of conduct Citation

AdaptiveRegularization

AdaptiveRegularization is a solver for unconstrained nonlinear problems,

julia min f(x)

It uses other JuliaSmoothOptimizers packages for development. In particular, NLPModels.jl is used for defining the problem, and SolverCore.jl for the output.

This package uses Stopping.jl via NLPStopping to handle its workflow, you can also see tutorials with Stopping to learn more.

Algorithm

The initial implementation of this package follows (Dussault, J.-P. 2020):

Adaptive cubic regularization (ARC) and trust-region (TR) methods use modified linear systems to compute their steps. The modified systems consist in adding some multiple of the identity matrix (or a well-chosen positive definite matrix) to the Hessian to obtain a sufficiently positive definite linear system, the so called shifted system. This type of system was first proposed by Levenberg and Marquardt. Some trial and error is often involved to obtain a specified value for this shift parameter. We provide an efficient unified implementation to track the shift parameter; our implementation encompasses many ARC and TR variants.

References

Dussault, J.-P. (2020). A unified efficient implementation of trust-region type algorithms for unconstrained optimization. INFOR: Information Systems and Operational Research, 58(2), 290-309. 10.1080/03155986.2019.1624490

Dussault, J.-P., Migot, T. & Orban, D. (2023). Scalable adaptive cubic regularization methods. Mathematical Programming. 10.1007/s10107-023-02007-6

How to Cite

If you use AdaptiveRegularization.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

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!

cff-version: 1.2.0
title: >-
  AdaptiveRegularization.jl: A unified efficient
  implementation of trust-region type algorithms for
  unconstrained optimization
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Jean-Pierre
    family-names: Dussault
    email: Jean-Pierre.Dussault@usherbrooke.ca
    affiliation: Université de Sherbrooke
    orcid: 'https://orcid.org/0000-0001-7253-7462'
  - given-names: Samuel
    family-names: Goyette
    email: samuel.goyette@usherbrooke.ca
    affiliation: Université de Sherbrooke
  - given-names: Tangi
    family-names: Migot
    email: tangi.migot@gmail.com
    affiliation: Polytechnique Montréal
    orcid: 'https://orcid.org/0000-0001-7729-2513'
  - given-names: Dominique
    family-names: Orban
    email: dominique.orban@gerad.ca
    affiliation: >-
      GERAD and Department of Mathematics and
      Industrial Engineering, Polytechnique
      Montréal,             QC, Canada
    orcid: 'https://orcid.org/0000-0002-8017-7687'
  - given-names: contributors
identifiers:
  - type: doi
    value: 10.5281/zenodo.10434673
    description: Zenodo all versions
repository-code: >-
  https://github.com/JuliaSmoothOptimizers/AdaptiveRegularization.jl
keywords:
  - Optimization
  - Nonlinear programming
  - Julia
  - Adaptive regularization with cubics
date-released: '2024-01-01'
license: MIT

GitHub Events

Total

Create event: 20
Commit comment event: 4
Issues event: 1
Release event: 2
Delete event: 17
Issue comment event: 5
Push event: 50
Pull request review comment event: 1
Pull request review event: 3
Pull request event: 34

Last Year

Create event: 20
Commit comment event: 4
Issues event: 1
Release event: 2
Delete event: 17
Issue comment event: 5
Push event: 50
Pull request review comment event: 1
Pull request review event: 3
Pull request event: 34

Issues and Pull Requests

Last synced: 10 months ago

All Time

Total issues: 16
Total pull requests: 122
Average time to close issues: 5 months
Average time to close pull requests: 9 days
Total issue authors: 4
Total pull request authors: 5
Average comments per issue: 0.69
Average comments per pull request: 0.55
Merged pull requests: 102
Bot issues: 1
Bot pull requests: 47

Past Year

Issues: 0
Pull requests: 33
Average time to close issues: N/A
Average time to close pull requests: 6 days
Issue authors: 0
Pull request authors: 4
Average comments per issue: 0
Average comments per pull request: 0.12
Merged pull requests: 23
Bot issues: 0
Bot pull requests: 18

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Top Authors

Issue Authors

tmigot (12)
github-actions[bot] (1)
JuliaTagBot (1)
bzhangcw (1)

Pull Request Authors

tmigot (74)
github-actions[bot] (43)
dependabot[bot] (5)
nrummel (2)
amontoison (2)

Top Labels

Issue Labels

bug (2) enhancement (2) good first issue (1) formatting (1) automated pr (1) no changelog (1)

Pull Request Labels

formatting (24) automated pr (24) no changelog (24) chore (8) dependencies (5) github_actions (5) documentation (5)

Packages

Total packages: 1
Total downloads:
- julia 1 total

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 3

juliahub.com: AdaptiveRegularization

Documentation: https://docs.juliahub.com/General/AdaptiveRegularization/stable/
License: MIT
Latest release: 0.2.0
published about 1 year ago

Versions: 3
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 1 Total

Rankings

Dependent repos count: 10.0%

Average: 25.1%

Dependent packages count: 40.2%

Last synced: 10 months ago

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