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change for JOSS paper #4

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Update paper.md
danielskatz Nov 30, 2024
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Update paper.bib
danielskatz Nov 30, 2024
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10 changes: 5 additions & 5 deletions paper/paper.bib
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@ @article{kraft1988software
}

@article{kraft1994algorithm,
title={Algorithm 733: TOMP--Fortran modules for optimal control calculations},
title={Algorithm 733: TOMP--{F}ortran modules for optimal control calculations},
author={Kraft, Dieter},
journal={ACM Transactions on Mathematical Software (TOMS)},
volume={20},
Expand All @@ -31,7 +31,7 @@ @article{gill2005snopt
}

@article{virtanen2020scipy,
title={SciPy 1.0: fundamental algorithms for scientific computing in Python},
title={SciPy 1.0: fundamental algorithms for scientific computing in {P}ython},
author={Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and others},
journal={Nature methods},
volume={17},
Expand All @@ -43,7 +43,7 @@ @article{virtanen2020scipy
}

@article{wu2020pyoptsparse,
title={pyOptSparse: A Python framework for large-scale constrained nonlinear optimization of sparse systems},
title={pyOptSparse: A {P}ython framework for large-scale constrained nonlinear optimization of sparse systems},
author={Wu, Neil and Kenway, Gaetan and Mader, Charles A and Jasa, John and Martins, Joaquim RRA},
journal={Journal of Open Source Software},
volume={5},
Expand All @@ -54,7 +54,7 @@ @article{wu2020pyoptsparse
}

@article{perez2012pyopt,
title={pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization},
title={pyOpt: a {P}ython-based object-oriented framework for nonlinear constrained optimization},
author={Perez, Ruben E and Jansen, Peter W and Martins, Joaquim RRA},
journal={Structural and Multidisciplinary Optimization},
volume={45},
Expand Down Expand Up @@ -84,4 +84,4 @@ @article{joshy2024modopt
journal={arXiv preprint arXiv:2410.12942},
year={2024},
doi={10.48550/arXiv.2410.12942}
}
}
14 changes: 6 additions & 8 deletions paper/paper.md
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@@ -1,5 +1,5 @@
---
title: '`PySLSQP`: A transparent Python package for the SLSQP optimization algorithm
title: 'PySLSQP: A transparent Python package for the SLSQP optimization algorithm
modernized with utilities for visualization and post-processing'
# modernized with utilities for scaling, recording, restarting, visualization, and post-processing'
tags:
Expand All @@ -14,12 +14,10 @@ authors:
# corresponding: true
affiliation: 1
- name: John T. Hwang
affiliation: 2
affiliation: 1
affiliations:
- name: PhD Candidate, Department of Mechanical and Aerospace Engineering, University of California San Diego
- name: Department of Mechanical and Aerospace Engineering, University of California San Diego, USA
index: 1
- name: Associate Professor, Department of Mechanical and Aerospace Engineering, University of California San Diego
index: 2
date: 6 August 2024
bibliography: paper.bib
---
Expand All @@ -39,13 +37,13 @@ solving a sequence of Quadratic Programming (QP) subproblems.
The Sequential Least SQuares Programming algorithm, or SLSQP, has been one of the
most widely used SQP algorithms since the 1980s.

We present `PySLSQP`, a seamless interface for using the SLSQP algorithm from Python,
We present `PySLSQP`, a seamless interface for using the SLSQP algorithm from Python
<!-- The `PySLSQP` package provides a seamless interface for using the SLSQP algorithm from Python. -->
that wraps the original Fortran code sourced from the SciPy repository and provides
a host of new features to improve the research utility of the original algorithm.
`PySLSQP` uses a simple yet modern workflow for compiling and using Fortran code from Python.
This allows even beginner developers to easily modify the algorithm in Fortran
for their specific needs and use in Python the wrapper auto-generated by the workflow.
for their specific needs and use, in Python, the wrapper auto-generated by the workflow.

Some of the additional features offered by `PySLSQP` include auto-generation of
unavailable derivatives using finite differences, independent scaling of the problem
Expand Down Expand Up @@ -360,4 +358,4 @@ please consult the [documentation](https://pyslsqp.readthedocs.io/).

This work was supported by NASA under award No. 80NSSC23M0217.

# References
# References
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