ShiftedProximalOperators
Proximal operators for use with RegularizedOptimization
https://github.com/juliasmoothoptimizers/shiftedproximaloperators.jl
Science Score: 67.0%
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Low similarity (10.6%) to scientific vocabulary
Keywords
Repository
Proximal operators for use with RegularizedOptimization
Basic Info
Statistics
- Stars: 7
- Watchers: 5
- Forks: 9
- Open Issues: 11
- Releases: 3
Topics
Metadata Files
README.md
ShiftedProximalOperators
How to cite
If you use ShiftedProximalOperators.jl in your work, please cite using the format given in CITATION.bib.
Synopsis
ShiftedProximalOperators is a library of proximal operators associated with proper lower-semicontinuous functions such as those implemented in ProximalOperators.jl for use in the algorithms implemented in RegularizedOptimization.jl.
The main difference between the proximal operators implemented in ProximalOperators.jl is that those implemented here involve a translation of the nonsmooth term. Specifically, this package considers proximal operators defined as
argmin { ½ ‖t - q‖₂² + ν h(x + s + t) + χ(s + t; ΔB) | t ∈ ℝⁿ },
where q is given, x and s are fixed shifts, h is the nonsmooth term with respect to which we are computing the proximal operator, and χ(.; ΔB) is the indicator of a ball of radius Δ defined by a certain norm.
How to Install
Until this package is registered, use
julia
pkg> add https://github.com/rjbaraldi/ShiftedProximalOperators.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
- Website: https://juliasmoothoptimizers.github.io
- Repositories: 63
- Profile: https://github.com/JuliaSmoothOptimizers
Infrastructure and Solvers for Continuous Optimization in Julia
Citation (CITATION.bib)
@Misc{baraldi-orban-shifted-proximal-operators-2022,
author = {R. Baraldi and D. Orban},
title = {{ShiftedProximalOperators.jl}: Proximal Operators for Regularized Optimization},
month = {February},
howpublished = {\url{https://github.com/JuliaSmoothOptimizers/ShiftedProximalOperators.jl}},
year = {2022},
DOI = {10.5281/zenodo.6940317},
}
GitHub Events
Total
- Create event: 7
- Commit comment event: 4
- Issues event: 4
- Delete event: 3
- Member event: 1
- Issue comment event: 18
- Push event: 24
- Pull request event: 15
- Pull request review event: 16
- Pull request review comment event: 25
- Fork event: 1
Last Year
- Create event: 7
- Commit comment event: 4
- Issues event: 4
- Delete event: 3
- Member event: 1
- Issue comment event: 18
- Push event: 24
- Pull request event: 15
- Pull request review event: 16
- Pull request review comment event: 25
- Fork event: 1
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 10
- Total pull requests: 93
- Average time to close issues: 3 months
- Average time to close pull requests: about 1 month
- Total issue authors: 6
- Total pull request authors: 11
- Average comments per issue: 0.5
- Average comments per pull request: 0.97
- Merged pull requests: 71
- Bot issues: 0
- Bot pull requests: 22
Past Year
- Issues: 4
- Pull requests: 16
- Average time to close issues: 1 day
- Average time to close pull requests: about 19 hours
- Issue authors: 4
- Pull request authors: 5
- Average comments per issue: 0.0
- Average comments per pull request: 0.31
- Merged pull requests: 13
- Bot issues: 0
- Bot pull requests: 3
Top Authors
Issue Authors
- dpo (6)
- geoffroyleconte (1)
- MaxenceGollier (1)
- MohamedLaghdafHABIBOULLAH (1)
- JuliaTagBot (1)
- nathanemac (1)
Pull Request Authors
- dpo (32)
- github-actions[bot] (24)
- rjbaraldi (12)
- MaxenceGollier (12)
- MohamedLaghdafHABIBOULLAH (11)
- geoffroyleconte (7)
- Joshua-Wolff (1)
- amontoison (1)
- farhadrclass (1)
- nathanemac (1)
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Packages
- Total packages: 1
- Total downloads: unknown
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 2
juliahub.com: ShiftedProximalOperators
Proximal operators for use with RegularizedOptimization
- Documentation: https://docs.juliahub.com/General/ShiftedProximalOperators/stable/
- License: MPL-2.0
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Latest release: 0.2.1
published over 1 year ago
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