Data

Tools

Indexes

Applications

Experiments

Open Source Science

RegularizedProblems

Test Cases for Regularized Optimization

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

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: arxiv.org, zenodo.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (13.3%) to scientific vocabulary

Keywords

julia optimization proximal-algorithms proximal-gradient-method proximal-operators proximal-regularization test-problems
Last synced: 6 months ago · JSON representation ·

Repository

Test Cases for Regularized Optimization

Basic Info
  • Host: GitHub
  • Owner: JuliaSmoothOptimizers
  • License: other
  • Language: Julia
  • Default Branch: main
  • Homepage:
  • Size: 603 KB
Statistics
  • Stars: 5
  • Watchers: 2
  • Forks: 7
  • Open Issues: 14
  • Releases: 3
Topics
julia optimization proximal-algorithms proximal-gradient-method proximal-operators proximal-regularization test-problems
Created over 4 years ago · Last pushed 6 months ago
Metadata Files
Readme License Citation

README.md

RegularizedProblems.jl

CI codecov DOI

How to cite

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

Synopsis

RegularizedProblems is a repository of optimization problems implemented in pure Julia. Contrary to what the name suggests, the problems are not regularized but they should be. However, the choice of regularizer is left to the user.

The problems concerned by the package have the form

minimize f(x) + h(x)

where f: ℝⁿ → ℝ has Lipschitz-continuous gradient and h: ℝⁿ → ℝ is lower semi-continuous and proper. The smooth term f describes the objective to minimize while the role of the regularizer h is to select a solution with desirable properties: minimum norm, sparsity below a certain level, maximum sparsity, etc.

This repository gives access to several f terms. Regularizers h should be taken from ProximalOperators.jl.

How to Install

Until this package is registered, use julia pkg> add https://github.com/optimizers/RegularizedProblems.jl

What is Implemented?

Please refer to the documentation.

Related Software

References

  • A. Y. Aravkin, R. Baraldi and D. Orban, A Proximal Quasi-Newton Trust-Region Method for Nonsmooth Regularized Optimization, SIAM Journal on Optimization, 32(2), pp.900–929, 2022. Technical report: https://arxiv.org/abs/2103.15993

bibtex @article{aravkin-baraldi-orban-2022, author = {Aravkin, Aleksandr Y. and Baraldi, Robert and Orban, Dominique}, title = {A Proximal Quasi-{N}ewton Trust-Region Method for Nonsmooth Regularized Optimization}, journal = {SIAM Journal on Optimization}, volume = {32}, number = {2}, pages = {900--929}, year = {2022}, doi = {10.1137/21M1409536}, abstract = { We develop a trust-region method for minimizing the sum of a smooth term (f) and a nonsmooth term (h), both of which can be nonconvex. Each iteration of our method minimizes a possibly nonconvex model of (f + h) in a trust region. The model coincides with (f + h) in value and subdifferential at the center. We establish global convergence to a first-order stationary point when (f) satisfies a smoothness condition that holds, in particular, when it has a Lipschitz-continuous gradient, and (h) is proper and lower semicontinuous. The model of (h) is required to be proper, lower semi-continuous and prox-bounded. Under these weak assumptions, we establish a worst-case (O(1/\epsilon^2)) iteration complexity bound that matches the best known complexity bound of standard trust-region methods for smooth optimization. We detail a special instance, named TR-PG, in which we use a limited-memory quasi-Newton model of (f) and compute a step with the proximal gradient method, resulting in a practical proximal quasi-Newton method. We establish similar convergence properties and complexity bound for a quadratic regularization variant, named R2, and provide an interpretation as a proximal gradient method with adaptive step size for nonconvex problems. R2 may also be used to compute steps inside the trust-region method, resulting in an implementation named TR-R2. We describe our Julia implementations and report numerical results on inverse problems from sparse optimization and signal processing. Both TR-PG and TR-R2 exhibit promising performance and compare favorably with two linesearch proximal quasi-Newton methods based on convex models. } }

Owner

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

Infrastructure and Solvers for Continuous Optimization in Julia

Citation (CITATION.bib) 

@Misc{baraldi-orban-regularized-problems-2022,
  author = {R. Baraldi and D. Orban},
  title = {{RegularizedProblems.jl}: Test Cases for Regularized Optimization},
  month = {February},
  howpublished = {\url{https://github.com/JuliaSmoothOptimizers/RegularizedProblems.jl}},
  year = {2022},
  DOI = {10.5281/zenodo.6940315},
}

GitHub Events

Total
  • Create event: 6
  • Commit comment event: 1
  • Issues event: 4
  • Delete event: 7
  • Issue comment event: 12
  • Push event: 21
  • Pull request event: 14
  • Pull request review event: 12
  • Pull request review comment event: 17
  • Fork event: 1
Last Year
  • Create event: 6
  • Commit comment event: 1
  • Issues event: 4
  • Delete event: 7
  • Issue comment event: 12
  • Push event: 21
  • Pull request event: 14
  • Pull request review event: 12
  • Pull request review comment event: 17
  • Fork event: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 12
  • Total pull requests: 64
  • Average time to close issues: 4 days
  • Average time to close pull requests: 22 days
  • Total issue authors: 5
  • Total pull request authors: 10
  • Average comments per issue: 0.83
  • Average comments per pull request: 1.05
  • Merged pull requests: 52
  • Bot issues: 0
  • Bot pull requests: 11
Past Year
  • Issues: 3
  • Pull requests: 15
  • Average time to close issues: N/A
  • Average time to close pull requests: 5 days
  • Issue authors: 2
  • Pull request authors: 5
  • Average comments per issue: 0.33
  • Average comments per pull request: 0.27
  • Merged pull requests: 11
  • Bot issues: 0
  • Bot pull requests: 1
View more stats
Top Authors
Issue Authors
  • dpo (8)
  • tmigot (1)
  • MaxenceGollier (1)
  • JuliaTagBot (1)
  • nathanemac (1)
Pull Request Authors
  • dpo (35)
  • github-actions[bot] (15)
  • geoffroyleconte (6)
  • tmigot (5)
  • nathanemac (5)
  • rjbaraldi (3)
  • MohamedLaghdafHABIBOULLAH (3)
  • Vanadjy (2)
  • MaxenceGollier (2)
  • Joshua-Wolff (1)
Top Labels
Issue Labels
documentation (2)
Pull Request Labels
formatting (15) automated pr (15) no changelog (15)

Packages

  • Total packages: 1
  • Total downloads: unknown
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
juliahub.com: RegularizedProblems

Test Cases for Regularized Optimization

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 9.9%
Forks count: 24.5%
Average: 29.6%
Dependent packages count: 38.9%
Stargazers count: 45.1%
Last synced: 6 months ago