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[PRE REVIEW]: PySLSQP: A transparent Python package for the SLSQP optimization algorithm modernized with utilities for visualization and post-processing #7138
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👋 @anugrahjo - note that your paper does not compile. Please follow the example paper and note that you can click on the error above to find out more about it. (In this case, you have an invalid DOI. If there is no DOI for an author, remove the DOI line.) In addition, you could work on the possibly missing DOI that editorialbot suggests, but note that it may be incorrect, as well as fixing the DOIs with the extra prefix. Please feel free to make changes to your .bib file, then use the command Also, your paper is 2700 words long, and JOSS papers are intended to be 250-1000 words, so you will have to substantially cut your paper. Again, see the example paper and the JOSS documentation for what a JOSS paper should be. Finally, looking at the code itself, it appears the Fortran code here is not an original contribution, but the Python code is. Is this correct? |
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Five most similar historical JOSS papers: PyCUTEst: an open source Python package of optimization test problems BayesO: A Bayesian optimization framework in Python Enlsip.jl: A Julia optimization package to solve constrained nonlinear least-squares problems FitBenchmarking: an open source Python package comparing data fitting software PySensors: A Python package for sparse sensor placement |
👋 @danielskatz Thank you for the feedback. I have made the changes to the references and the paper compiles now. Sorry about the length of the paper. One of the JOSS papers in our list of references was of similar length so we thought the 250-1000 word length was a suggestion. We can reduce the length if necessary. Regarding the code contribution, yes, the Python code is the main contribution and the build workflows for autogenerating the precompiled binaries for the wrapped Fortran code. The Fortran code for SLSQP itself is open-source and in our case, it is sourced from the SciPy repository but is also available from many other sources. |
Paper length is one of many things in JOSS that has gotten stricter over time... Please let me know when you have a shorter paper. I'll mark this as paused for now. |
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@anugrahjo - before you do any extra work (on shortening the paper), I'm going to ask the JOSS editors to take a look at this for scope, since the commit history here and in https://github.com/anugrahjo/PySLSQP_alpha just goes back about 3 months. There should be a decision on this in a week or two. |
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@danielskatz That's great! Thanks for the update. |
@danielskatz I have capacity but this submission is out of my domain |
👋 @prashjha - Would you be willing to edit this submission? |
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@danielskatz happy to edit this submission! |
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Assigned! @prashjha is now the editor |
Hi @anugrahjo, could you please look at the JOSS reviewer database and suggest a few names who can review your submission? |
Hi @prashjha, sure. |
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@anugrahjo - Can I please get assigned to review this ? Thank you in advance. |
@prashjha - Could you check and assign the reviewers? Thanks. |
@prashjha - Just following up on this request. Could I get assigned to review this please? |
Hi @anugrahjo, sorry for the delay. Thank you for suggesting names. @hariharanragothaman and @saaikrishnan, thank you for your interest in reviewing the submission. |
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Five most similar historical JOSS papers: PyCUTEst: an open source Python package of optimization test problems BayesO: A Bayesian optimization framework in Python Enlsip.jl: A Julia optimization package to solve constrained nonlinear least-squares problems FitBenchmarking: an open source Python package comparing data fitting software Linopy: Linear optimization with n-dimensional labeled variables |
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OK, I've started the review over in #7246. |
Submitting author: @anugrahjo (Anugrah Jo Joshy)
Repository: https://github.com/anugrahjo/PySLSQP
Branch with paper.md (empty if default branch):
Version: v0.1.0
Editor: @prashjha
Reviewers: @hariharanragothaman, @saaikrishnan
Managing EiC: Daniel S. Katz
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