[REVIEW]: pyOptSparse: A Python framework for large-scale constrained nonlinear optimization of sparse systems

[whedon]

Submitting author: @ewu63 (Ella Wu)
Repository: https://github.com/mdolab/pyoptsparse/
Version: v2.2.1
Editor: @poulson
Reviewer: @jgoldfar, @vissarion, @matbesancon
Archive: 10.5281/zenodo.4110792

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Review checklist for @jgoldfar

Conflict of interest

• I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

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General checks

• Repository: Is the source code for this software available at the repository url?
• License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@nwu63) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
• Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines

Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

Review checklist for @vissarion

Conflict of interest

• I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

• I confirm that I read and will adhere to the JOSS code of conduct.

General checks

• Repository: Is the source code for this software available at the repository url?
• License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@nwu63) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
• Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines

Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

Review checklist for @matbesancon

Conflict of interest

• I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

• I confirm that I read and will adhere to the JOSS code of conduct.

General checks

• Repository: Is the source code for this software available at the repository url?
• License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@nwu63) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
• Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines

Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

[whedon]

Hello human, I'm @whedon, a robot that can help you with some common editorial tasks. @jgoldfar, @vissarion, @matbesancon it looks like you're currently assigned to review this paper 🎉.

⚠️ JOSS reduced service mode ⚠️

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[whedon]

PDF failed to compile for issue #2564 with the following error:

Can't find any papers to compile :-(

[poulson]

⚠️ As noted by @vissarion and @matbesancon in the pre-review, both of them expect to be unable to start their review until September. ⚠️

[poulson]

@whedon generate pdf from branch paper

[whedon]

Attempting PDF compilation from custom branch paper. Reticulating splines etc...

[whedon]

👉 Check article proof 📄 👈

[matbesancon]

@whedon remind @matbesancon in 3 weeks

[whedon]

I'm sorry @matbesancon, I'm afraid I can't do that. That's something only editors are allowed to do.

[matbesancon]

1. Comparisons

I took a preliminary look at the paper, it would be interesting to compare the features with other optimization packages including solvers like Ipopt, SCIP and their Python bindings and solver-agnostic modelling languages like Pyomo or JuMP.
Is PyOpt more a solving or modeling package. If it is of the latter, what solvers are currently supported and how easy is it to switch between solvers?

2. Usage

Their could be a section focused more on usage of the package. Non-linear optimization is notoriously hard to make user-friendly. There could be a representative usage example. How are non-linear functions passed to the packages? Are arbitrary Python functions supported?

[ewu63]

Hi Mathieu, thanks for taking your time to do this review.

1. pyOptSparse is a wrapper for several popular optimizers, with a particular focus on large-scale nonlinear programming of continuous variables. As such, it is closer to frameworks such as pyomo and scipy, where the optimization problem is defined in a particular syntax and pyOptSparse performs the conversion to the specific form required for each optimizer. The main distinguishing feature for pyOptSparse is the support for sparse constraint Jacobians, both for linear and nonlinear constraints, and particularly for supporting sparse sub-blocks of the Jacobian. Through the unified framework, it's straightforward to switch between say SNOPT and IPOPT by changing one line of code, and all conversion will be done automatically including converting the sparse Jacobian into a different format. There are also other features such as the ability to perform finite-difference or complex-step to compute derivatives automatically, and MPI support for parallel function evaluations.

   The list of solvers is shown in the documentation (for example the side bar here). To switch between optimizers, the user would need to 1) change the options dictionary, and 2) change the name of the optimizer in the optimize call, for example replacing SLSQP with IPOPT in the line opt = OPT("SLSQP", options=optOptions). I hesitate to provide a list and description of the optimizers in the paper because the list may change over time, with older optimizers eventually become deprecated and new ones added.

2. Yes, arbitrary Python functions are supported, and the data is stored in a dictionary. A nested dictionary is used for gradients if they are supplied. It's a little bit unclear for me what type of information should be in the paper, and what should be in the documentation site of the repository. I would prefer, if possible, to keep most of this information in the documentation since API may change and users typically will refer to that instead of the journal paper for help on using the package. But please let me know if JOSS takes a different view and I'd be happy to add more information to the paper. For reference, please see the Quickstart for a basic example, and the Guide for a complete example with sparse linear and nonlinear constraints.

[matbesancon]

I agree that listing the solvers is not super relevant to the paper which should be more of a permanent document presenting the package. The fact that multiple solvers can be used with the same uniform syntax should be highlighted more.

There are also other features such as the ability to perform finite-difference or complex-step to compute derivatives automatically, and MPI support for parallel function evaluations.

I think this could be presented in the paper, as it can be of interest to the reader comparing different frameworks.

For the second point, I think more should go in the paper without going into the internal details that are likely to change. I think having at least a high-level example with arbitrary Python functions would be of value to the article

[ewu63]

Hi Mathieu, I agree with your points and I will push an update to the paper soon.

[vissarion]

I finished my review.

I agree with the points listed above. I would also like to see a high-level example probably with the comment that links to the documentation for more details.

One more comment, I think it would be useful to note in the paper the current release version of the package.

[ewu63]

Hi Mathieu and Vissarion,

Thanks for your feedback and apologies for not replying sooner, but I have been working with co-authors to update the paper. There are some substantial changes:

• I have added a Features section to explain in more detail some salient features of the software which are not found elsewhere
• I have added a section containing an example optimization script. There is no guarantee that the public API would remain stable, but I do think it's useful in showing what an optimization script would look like.
• I have addressed other, more minor comments

The paper is located under the paper branch of the repo, please take a look and let me know if you have any feedback.

Cheers,
Neil

[vissarion]

Thanks for the substantial revision Neil. The paper now looks much better and useful for the reader. I do not have further comments.

[matbesancon]

@whedon generate pdf from branch paper

[whedon]

Attempting PDF compilation from custom branch paper. Reticulating splines etc...

[whedon]

👉 Check article proof 📄 👈

[matbesancon]

Posted on mdolab/pyoptsparse#145 for the testing part which seems to have issues

[matbesancon]

Ok nevermind the tests do work. On the installation instructions here, I would recommend you state upfront that a fortran compiler is needed (maybe recommend gfortran directly)

[ewu63]

I've updated the installation instructions in this PR.

[matbesancon]

perfect thanks!

[whedon]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[poulson]

@whedon set 10.5281/zenodo.4110792 as archive

[whedon]

OK. 10.5281/zenodo.4110792 is the archive.

[poulson]

@whedon set v2.2.1 as version

[whedon]

OK. v2.2.1 is the version.

[poulson]

@whedon accept

[whedon]

Attempting dry run of processing paper acceptance...

[whedon]

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.1007/s00158-011-0666-3 is OK
- 10.2514/1.J056603 is OK
- 10.2514/1.C034934 is OK
- 10.5194/wes-4-163-2019 is OK
- 10.1007/s00158-019-02211-z is OK
- 10.1007/978-3-319-97773-7_38 is OK
- 10.1017/aer.2018.120 is OK
- 10.1038/s41592-019-0686-2 is OK
- 10.1145/838250.838251 is OK

MISSING DOIs

- None

INVALID DOIs

- None

[whedon]

👋 @openjournals/joss-eics, this paper is ready to be accepted and published.

Check final proof 👉 openjournals/joss-papers#1835

If the paper PDF and Crossref deposit XML look good in openjournals/joss-papers#1835, then you can now move forward with accepting the submission by compiling again with the flag deposit=true e.g.

@whedon accept deposit=true

[ewu63]

@poulson is there anything I need to do here? Or will the EiC take care of this when they get a chance?

[kyleniemeyer]

@nwu63 yes, I will take care of this today

[kyleniemeyer]

@whedon accept deposit=true

[whedon]

Doing it live! Attempting automated processing of paper acceptance...

[whedon]

🐦🐦🐦 👉 Tweet for this paper 👈 🐦🐦🐦

[whedon]

🚨🚨🚨 THIS IS NOT A DRILL, YOU HAVE JUST ACCEPTED A PAPER INTO JOSS! 🚨🚨🚨

Here's what you must now do:

0. Check final PDF and Crossref metadata that was deposited 👉 Creating pull request for 10.21105.joss.02564 joss-papers#1850
1. Wait a couple of minutes to verify that the paper DOI resolves https://doi.org/10.21105/joss.02564
2. If everything looks good, then close this review issue.
3. Party like you just published a paper! 🎉🌈🦄💃👻🤘

Any issues? Notify your editorial technical team...

[kyleniemeyer]

Congrats @nwu63 on your article's publication in JOSS!

Many thanks to @jgoldfar, @vissarion, and @matbesancon for reviewing this submission, and @poulson for editing.

[whedon]

🎉🎉🎉 Congratulations on your paper acceptance! 🎉🎉🎉

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[ewu63]

Hi @kyleniemeyer and everyone else involved in this, thanks so much! Just one quick question: there is an incorrect capitalization in the author list here, Joaquim R. r. a. Martins should be Joaquim R. R. A. Martins, is there a way to fix that?

[kyleniemeyer]

@openjournals/dev can you help with this? the issue is in the author list on the landing page—the paper itself is fine

[arfon]

@openjournals/dev can you help with this? the issue is in the author list on the landing page—the paper itself is fine

Fixed.

[editorialbot]

🌈 Paper updated!

New PDF and metadata files 👉 openjournals/joss-papers#5321