Paper draft #205

MohamedLaghdafHABIBOULLAH · 2025-09-05T22:59:17Z

Here is the first version of the JOSS paper @dpo @MaxenceGollier and Youssef.

Please note that the draft-paper.yml workflow file should be updated to work with the latest versions and to run on pull requests.

Here is the pdf: paper.pdf

codecov · 2025-09-06T14:48:55Z

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paper/paper.bib

paper/paper.md

dpo · 2025-09-06T21:29:06Z

This branch must be rebased.

Co-authored-by: Maxence Gollier <134112149+MaxenceGollier@users.noreply.github.com>

MohamedLaghdafHABIBOULLAH · 2025-09-07T13:25:41Z

@dpo you may now begin the review of the paper.

dpo · 2025-09-07T15:28:02Z

@MohamedLaghdafHABIBOULLAH You could also remove cirrus.yml from this branch.

paper/paper.md

MohamedLaghdafHABIBOULLAH · 2025-09-08T19:00:46Z

@Loptima here is the joss paper.

dpo · 2025-09-15T15:16:27Z

@MohamedLaghdafHABIBOULLAH Could you please add a workflow so the PDF is posted in the pull request?

MohamedLaghdafHABIBOULLAH · 2025-09-15T17:23:57Z

In fact, I tried it but it doesn’t work.

MaxenceGollier · 2025-09-16T17:37:18Z

can you rebase @MohamedLaghdafHABIBOULLAH ?

MaxenceGollier

Just a quick review for now, other comments will follow

MaxenceGollier · 2025-09-16T17:39:45Z

.github/workflows/draft-pdf.yml

+      - name: Create release
+        if: github.event_name == 'push'
+        uses: rymndhng/release-on-push-action@master
+        id: release
+        with:
+          bump_version_scheme: patch
+          tag_prefix: v
+          release_body: ""
+          use_github_release_notes: true
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Upload PDF to release
+        if: github.event_name == 'push'
+        uses: svenstaro/upload-release-action@v2
+        with:
+          repo_token: ${{ secrets.GITHUB_TOKEN }}
+          file: paper/paper.pdf
+          asset_name: joss-draft.pdf
+          tag: ${{ steps.release.outputs.tag_name }}
+          overwrite: true


Actually, it looks like it does not work.
This is weird, you might need to remove I am not sure.

MaxenceGollier · 2025-09-16T17:40:42Z

paper/paper.md

-# References
+# Summary
+
+[RegularizedOptimization.jl](https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl) is a Julia [@bezanson-edelman-karpinski-shah-2017] package that implements a family of regularization and trust-region type algorithms for solving nonsmooth optimization problems of the form:


Suggested change

[RegularizedOptimization.jl](https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl) is a Julia [@bezanson-edelman-karpinski-shah-2017] package that implements a family of regularization and trust-region type algorithms for solving nonsmooth optimization problems of the form:

[RegularizedOptimization.jl](https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl) is a Julia [@bezanson-edelman-karpinski-shah-2017] package that implements a family of quadratic regularization and trust-region type algorithms for solving nonsmooth optimization problems of the form:

MaxenceGollier · 2025-09-16T17:45:24Z

paper/paper.md

+
+- **Trust-region methods (TR, TRDH)** [@aravkin-baraldi-orban-2022] and [@leconte-orban-2023],
+- **Quadratic regularization methods (R2, R2N)** [@diouane-habiboullah-orban-2024] and [@aravkin-baraldi-orban-2022],
+- **Levenbergh-Marquardt methods (LM, LMTR)** [@aravkin-baraldi-orban-2024].


Suggested change

- **Levenbergh-Marquardt methods (LM, LMTR)** [@aravkin-baraldi-orban-2024].

- **Levenberg-Marquardt methods (LM, LMTR)** [@aravkin-baraldi-orban-2024].

MaxenceGollier · 2025-09-16T17:46:24Z

paper/paper.md

+
+These methods rely solely on the gradient and Hessian(-vector) information of the smooth part $f$ and the proximal mapping of the nonsmooth part $h$ in order to compute steps.
+Then, the objective function $f + h$ is used only to accept or reject trial points.
+Moreover, they can handle cases where Hessian approximations are unbounded[@diouane-habiboullah-orban-2024] and [@leconte-orban-2023-2], making the package particularly suited for large-scale, ill-conditioned, and nonsmooth problems.


Suggested change

Moreover, they can handle cases where Hessian approximations are unbounded[@diouane-habiboullah-orban-2024] and [@leconte-orban-2023-2], making the package particularly suited for large-scale, ill-conditioned, and nonsmooth problems.

Moreover, they can handle cases where Hessian approximations are unbounded [@diouane-habiboullah-orban-2024] and [@leconte-orban-2023-2], making the package particularly suited for large-scale, ill-conditioned, and nonsmooth problems.

MaxenceGollier · 2025-09-16T17:47:22Z

paper/paper.md

+
+## Model-based framework for nonsmooth methods
+
+There exists a way to solve \eqref{eq:nlp} in Julia using [ProximalAlgorithms.jl](https://github.com/JuliaFirstOrder/ProximalAlgorithms.jl), which implements in-place first-order line search–based methods for composite optimization.


composite has not been defined

MaxenceGollier · 2025-09-16T20:01:00Z

paper/paper.md

+stats = RegularizedExecutionStats(reg_nlp)
+
+# Solve the problem
+solve!(solver_tr, reg_nlp, stats, x = f.meta.x0, atol = 1e-3, rtol = 1e-4, verbose = 10, ν = 1.0e+2)


This test does not work locally.
For example $$\nu$$ is not a kwarg for TR.

MohamedLaghdafHABIBOULLAH force-pushed the paper-draft branch from e96129f to 5b9e859 Compare September 5, 2025 23:04

MaxenceGollier reviewed Sep 6, 2025

View reviewed changes

MohamedLaghdafHABIBOULLAH and others added 7 commits September 6, 2025 17:53

add paper example

ebd13d6

minor error

158fc0a

add bib

8c75806

correct the typos and bib

595c372

Update GitHub Actions workflow for PDF generation

49cf76b

Update draft-pdf.yml

3722a04

Apply suggestions from code review

2bb4f05

Co-authored-by: Maxence Gollier <134112149+MaxenceGollier@users.noreply.github.com>

MohamedLaghdafHABIBOULLAH force-pushed the paper-draft branch from fca3a30 to 2bb4f05 Compare September 6, 2025 21:54

incorporate suggestions

33510b3

MohamedLaghdafHABIBOULLAH requested a review from MaxenceGollier September 7, 2025 12:59

MohamedLaghdafHABIBOULLAH added 2 commits September 7, 2025 09:05

add opt bib

3b67bb7

correct the title

afd7753

dpo requested changes Sep 7, 2025

View reviewed changes

incorporate Dominique comments

269bad8

MohamedLaghdafHABIBOULLAH force-pushed the paper-draft branch from 0df3739 to 269bad8 Compare September 8, 2025 04:53

MohamedLaghdafHABIBOULLAH requested a review from dpo September 8, 2025 04:56

Add the examples

bc756ee

MohamedLaghdafHABIBOULLAH force-pushed the paper-draft branch from 6373690 to bc756ee Compare September 8, 2025 16:52

clarify the comparison

3c68da0

MohamedLaghdafHABIBOULLAH added 2 commits September 15, 2025 14:10

add first results on fh

b06581b

Test if the workflow works

3754018

MaxenceGollier reviewed Sep 16, 2025

View reviewed changes

	[RegularizedOptimization.jl](https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl) is a Julia [@bezanson-edelman-karpinski-shah-2017] package that implements a family of regularization and trust-region type algorithms for solving nonsmooth optimization problems of the form:
	[RegularizedOptimization.jl](https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl) is a Julia [@bezanson-edelman-karpinski-shah-2017] package that implements a family of quadratic regularization and trust-region type algorithms for solving nonsmooth optimization problems of the form:

	- Levenbergh-Marquardt methods (LM, LMTR) [@aravkin-baraldi-orban-2024].
	- Levenberg-Marquardt methods (LM, LMTR) [@aravkin-baraldi-orban-2024].

	Moreover, they can handle cases where Hessian approximations are unbounded[@diouane-habiboullah-orban-2024] and [@leconte-orban-2023-2], making the package particularly suited for large-scale, ill-conditioned, and nonsmooth problems.
	Moreover, they can handle cases where Hessian approximations are unbounded [@diouane-habiboullah-orban-2024] and [@leconte-orban-2023-2], making the package particularly suited for large-scale, ill-conditioned, and nonsmooth problems.


		## Model-based framework for nonsmooth methods

		There exists a way to solve \eqref{eq:nlp} in Julia using [ProximalAlgorithms.jl](https://github.com/JuliaFirstOrder/ProximalAlgorithms.jl), which implements in-place first-order line search–based methods for composite optimization.

Paper draft #205

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