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16 changes: 3 additions & 13 deletions paper/paper.tex
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Expand Up @@ -27,22 +27,16 @@ \section{Introduction}
\end{equation}
where \(f:\mathbb{R}^n\to\mathbb{R}\) and \(c:\mathbb{R}^n\to\mathbb{R}^m\) are continuously differentiable, with \(c_L \in \left(\mathbb{R} \cup \{-\infty\} \right)^{m}\), \(c_U \in \left(\mathbb{R} \cup \{+\infty\} \right)^{m}\), \(\ell \in \left(\mathbb{R} \cup \{-\infty\} \right)^n\), and \(u \in \left(\mathbb{R} \cup \{+\infty\} \right)^n\).
Bounds on the variables appear separately from other types of constraints because numerical methods often treat them differently.
%Equality constraints are modeled by setting components \(-\infty < c_{L,i}=c_{U,i} < +\infty\) in~\eqref{eq:nlp}.

\section{Statement of Need}

\texttt{JSOSuite.jl} is part of the JuliaSmoothOptimizers (JSO) ecosystem, % \cite{jso}.
an academic organization that offers a collection of Julia packages for nonlinear optimization software development, testing, and benchmarking.

\texttt{JSOSuite.jl} is part of the JuliaSmoothOptimizers (JSO) ecosystem, an academic organization that offers a collection of Julia packages for nonlinear optimization software development, testing, and benchmarking.

JSO provides a general API for solvers to interact with models by providing flexible data types to represent the objective and constraint functions, evaluate their derivatives, and provide essentially any information that a solver might request from a model.
%
\texttt{NLPModels.jl} \cite{orban-siqueira-nlpmodels-2020} is the core package that defines the abstract type \texttt{AbstractNLPModel} and introduces the API, including in-place and out-of-place evaluation of the objective gradient, sparse Jacobian and Hessian matrices as well as operators %\cite{orban-siqueira-linearoperators-2020}
for matrix-free implementations.
\texttt{NLPModels.jl} \cite{orban-siqueira-nlpmodels-2020} is the core package that defines the abstract type \texttt{AbstractNLPModel} and introduces the API, including in-place and out-of-place evaluation of the objective gradient, sparse Jacobian and Hessian matrices as well as operators for matrix-free implementations.
%
The user can hand-code derivatives %\cite{Siqueira_ManualNLPModels_NLPModels_where_2022}
, use automatic differentiation \cite{orban-siqueira-nlpmodels-2021}, or use JSO interfaces to optimization modeling languages such as AMPL \cite{fourer2003ampl}, CUTEst \cite{cutest}, or JuMP \cite{jump}.
% \cite{orban-siqueira-amplnlreader-2020,orban-siqueira-cutest-2020,montoison-orban-siquiera-nlpmodelsjump-2020}
The user can hand-code derivatives, use automatic differentiation \cite{orban-siqueira-nlpmodels-2021}, or use JSO interfaces to optimization modeling languages such as AMPL \cite{fourer2003ampl}, CUTEst \cite{cutest}, or JuMP \cite{jump}.
%
Hence, solvers can be designed to rely on the API's behavior independently of the problem's origin.

Expand All @@ -54,8 +48,6 @@ \section{Statement of Need}
Most of those solvers also have variants for nonlinear least squares.
A solver for convex quadratic programs is in \texttt{RipQP.jl} \cite{orban-leconte-ripqp-2020}.
The solvers mentioned are pure Julia implementations, but there also exist thin wrappers to well-established solvers such as \texttt{Artelys Knitro} \cite{byrd2006k} and \texttt{Ipopt} \cite{wachter2006implementation}.
% \texttt{Algencan} \cite{Andreani2008}
% \cite{NLPModelsAlgencan,orban-siqueira-nlpmodelsknitro-2020,orban-siqueira-nlpmodelsipopt-2020}

The aim of \texttt{JSOSuite.jl} is to provide a unique interface to navigate among the available solvers and select the best one without knowing all the JSO-compliant solvers.
%
Expand Down Expand Up @@ -107,8 +99,6 @@ \section{Functionalities}

A list of parameters common to all JSO-compliant solvers is maintained, and can be passed to \texttt{minimize}.
%
%\texttt{JSOSuite.jl} also tests that pure Julia implementation run with arbitrary arithmetic.
%
Combined with \texttt{SolverBenchmark.jl} \cite{orban-siqueira-solverbenchmark-2020}, it can seamlessly compare algorithms and generate data and performance profiles on collection of test problems.
Additionally, the package implements strategies that are classical for continuous optimization solvers such as methods to find a feasible initial guess or run a multi-start strategy.

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200 changes: 0 additions & 200 deletions paper/ref.bib
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Expand Up @@ -31,18 +31,6 @@ @software{orban-siqueira-nlpmodels-2020
year = {2023}
}

@article{montoison-orban-krylov-2020,
author = {Montoison, Alexis and Orban, Dominique},
doi = {10.21105/joss.05187},
journal = {Journal of Open Source Software},
number = {89},
pages = {5187},
title = {{Krylov.jl: A Julia basket of hand-picked Krylov methods}},
url = {https://joss.theoj.org/papers/10.21105/joss.05187},
volume = {8},
year = {2023}
}

@software{orban-siqueira-nlpmodels-2021,
author = {Migot, Tangi and Montoison, Alexis and Orban, Dominique and Soares Siqueira, Abel and contributors},
license = {MPL-2.0},
Expand All @@ -53,15 +41,6 @@ @software{orban-siqueira-nlpmodels-2021
year = {2023}
}

@software{Siqueira_ManualNLPModels_NLPModels_where_2022,
author = {Siqueira, Abel Soares},
doi = {10.5281/zenodo.6940304},
url = {https://github.com/JuliaSmoothOptimizers/ManualNLPModels.jl},
title = {{ManualNLPModels: NLPModels where all functions are defined by the user}},
version = {0.1.1},
year = {2022}
}

@article{bezanson2017julia,
title={Julia: A fresh approach to numerical computing},
author={Bezanson, Jeff and Edelman, Alan and Karpinski, Stefan and Shah, Viral B.},
Expand All @@ -74,44 +53,10 @@ @article{bezanson2017julia
doi={10.1137/141000671}
}

@article{lubin2015computing,
title={Computing in operations research using {J}ulia},
author={Lubin, Miles and Dunning, Iain},
journal={INFORMS Journal on Computing},
volume={27},
number={2},
pages={238--248},
year={2015},
publisher={INFORMS},
doi={10.1287/ijoc.2014.0623}
}

@software{orban-siqueira-nlpmodelsknitro-2020,
author = {Migot, Tangi and Orban, Dominique and Soares Siqueira, Abel and contributors},
license = {MPL-2.0},
title = {{NLPModelsKnitro.jl: A thin KNITRO wrapper for NLPModels}},
url = {https://github.com/JuliaSmoothOptimizers/NLPModelsKnitro.jl},
DOI = {10.5281/zenodo.3994983},
version = {0.8.0},
year = {2023}
}

@software{orban-siqueira-nlpmodelsipopt-2020,
author = {Orban, Dominique and Soares Siqueira, Abel and contributors},
license = {MPL-2.0},
title = {{NLPModelsIpopt.jl: A thin IPOPT wrapper for NLPModels}},
url = {https://github.com/JuliaSmoothOptimizers/NLPModelsIpopt.jl},
DOI = {10.5281/zenodo.2629034},
version = {0.10.1},
year = {2023}
}

@Inbook{byrd2006k,
author="Byrd, Richard H.
and Nocedal, Jorge
and Waltz, Richard A.",
editor="Di Pillo, G.
and Roma, M.",
title="Knitro: An integrated package for nonlinear optimization",
bookTitle="Large-Scale Nonlinear Optimization",
year="2006",
Expand All @@ -121,14 +66,6 @@ @Inbook{byrd2006k
doi="10.1007/0-387-30065-1",
}

@software{montoison-orban-siquiera-nlpmodelsjump-2020,
author = {Alexis Montoison and Dominique Orban and Abel Soares Siqueira and contributors},
title = {{NLPModelsJuMP.jl}: Conversion from {JuMP} models to {NLPModels}},
url = {https://github.com/JuliaSmoothOptimizers/NLPModelsJuMP.jl},
year = {2020},
DOI = {10.5281/zenodo.2574162},
}

@article{jump,
author = {Iain Dunning and Joey Huchette and Miles Lubin},
title = {{JuMP}: A Modeling Language for Mathematical Optimization},
Expand All @@ -149,25 +86,6 @@ @book{fourer2003ampl
doi = {10.1287/mnsc.36.5.519},
}

@software{orban-siqueira-amplnlreader-2020,
author = {Orban, Dominique and Soares Siqueira, Abel and contributors},
license = {MPL-2.0},
title = {{AmplNLReader.jl: A Julia interface to AMPL}},
url = {https://github.com/JuliaSmoothOptimizers/AmplNLReader.jl},
DOI = {10.5281/zenodo.3700941},
version = {0.11.2},
year = {2022}
}

@software{jso,
author = {Migot, Tangi and Orban, Dominique and Soares Siqueira, Abel and contributors},
license = {MPL-2.0},
title = {{The JuliaSmoothOptimizers ecosystem for numerical linear algebra and optimization in Julia}},
doi = {10.5281/zenodo.2655082},
url = {https://jso.dev},
year = {2024}
}

@software{Dussault_AdaptiveRegularization_jl_A_unified,
author = {Dussault, Jean-Pierre and Goyette, Samuel and Migot, Tangi and Orban, Dominique and contributors},
license = {MIT},
Expand Down Expand Up @@ -198,29 +116,6 @@ @software{orban-siqueira-solvercore-2021
year = {2023}
}

@article{migot-orban-siqueira-pdenlpmodels-2021,
author = {Migot, Tangi and Orban, Dominique and Soares Siqueira, Abel},
doi = {10.21105/joss.04736},
journal = {The Journal of Open Source Software},
number = {80},
pages = {4736},
title = {{PDENLPModels.jl: A NLPModel API for optimization problems with PDE-constraints}},
volume = {7},
year = {2022}
}

@article{dolan2002benchmarking,
title={Benchmarking optimization software with performance profiles},
author={Dolan, Elizabeth D. and Mor{\'e}, Jorge J},
journal={Mathematical programming},
volume={91},
number={2},
pages={201--213},
year={2002},
publisher={Springer},
doi={10.1007/s101070100263}
}

@software{orban-siqueira-solverbenchmark-2020,
author = {Orban, Dominique and Soares Siqueira, Abel and contributors},
license = {MPL-2.0},
Expand Down Expand Up @@ -253,34 +148,6 @@ @article{Orban_A_regularization_method_2020
year = {2020}
}

% https://github.com/JuliaDiff/ReverseDiff.jl/issues/136
@misc{reverseDiff,
title="{ReverseDiff.jl}",
year=2020,
author="Jarrett Revels",
url="https://github.com/JuliaDiff/ReverseDiff.jl"
}

@misc{NLPModelsAlgencan,
title="{NLPModelsAlgencan.jl}",
year=2023,
author="Paulo J. S. Silva and Ilan Francisco da Silva",
url="https://github.com/pjssilva/NLPModelsAlgencan.jl"
}

@article{Andreani2008,
doi = {10.1137/060654797},
url = {https://doi.org/10.1137/060654797},
year = {2008},
publisher = {Society for Industrial {\&} Applied Mathematics ({SIAM})},
volume = {18},
number = {4},
pages = {1286--1309},
author = {Roberto Andreani and Ernesto G. Birgin and Jose Mario Mart{\'{\i}}nez and Maria Laura Schuverdt},
title = {On Augmented Lagrangian Methods with General Lower-Level Constraints},
journal = {{SIAM} Journal on Optimization}
}

@software{orban-leconte-ripqp-2020,
author = {Dominique Orban and Geoffroy Leconte and contributors},
title = {{RipQP.jl}: Regularized Interior Point solver for Quadratic Problems},
Expand All @@ -289,16 +156,6 @@ @software{orban-leconte-ripqp-2020
DOI = {10.5281/zenodo.4309783},
}

@software{orban-siqueira-linearoperators-2020,
author = {Leconte, Geoffroy and Orban, Dominique and Soares Siqueira, Abel and contributors},
license = {MPL-2.0},
title = {{LinearOperators.jl: Linear Operators for Julia}},
url = {https://github.com/JuliaSmoothOptimizers/LinearOperators.jl},
DOI = {10.5281/zenodo.2559295},
version = {2.6.0},
year = {2023}
}

@software{Migot_FletcherPenaltySolver_jl_2022,
author = {Migot, Tangi and Orban, Dominique and Soares Siqueira, Abel and contributors},
license = {MPL-2.0},
Expand All @@ -321,63 +178,6 @@ @article{cutest
doi = {10.1007/s10589-014-9687-3},
}

@software{orban-siqueira-cutest-2020,
author = {Dominique Orban and Abel Soares Siqueira and contributors},
title = {{CUTEst.jl}: {J}ulia's {CUTEst} interface},
url = {https://github.com/JuliaSmoothOptimizers/CUTEst.jl},
year = {2020},
DOI = {10.5281/zenodo.1188851},
}

@software{orban-siqueira-optimizationproblems-2021,
author = {Migot, Tangi and Orban, Dominique and Soares Siqueira, Abel and contributors},
license = {MPL-2.0},
DOI = {10.5281/zenodo.3672094},
title = {{OptimizationProblems.jl: A collection of optimization problems in Julia}},
url = {https://github.com/JuliaSmoothOptimizers/OptimizationProblems.jl},
version = {0.7.3},
year = {2023}
}

@software{bogumil_kaminski_2023_8174885,
author = {Bogumił Kamiński and
John Myles White and
Milan Bouchet-Valat and
powerdistribution and
Sean Garborg and
Jacob Quinn and
Simon Kornblith and
cjprybol and
Alexey Stukalov and
Douglas Bates and
Tom Short and
Chris DuBois and
Harlan Harris and
Kevin Squire and
Alex Arslan and
pdeffebach and
David Anthoff and
Dave Kleinschmidt and
Andreas Noack and
Viral B. Shah and
Alex Ryckman Mellnik and
Takafumi Arakaki and
Tanmay Mohapatra and
Ronan Arraes Jardim Chagas and
Peter and
Stefan Karpinski and
Dahua Lin and
timema and
ExpandingMan and
Florian Oswald},
title = {JuliaData/DataFrames.jl: v1.6.1},
year = 2023,
publisher = {Zenodo},
version = {v1.6.1},
doi = {10.5281/zenodo.8174885},
url = {https://doi.org/10.5281/zenodo.8174885}
}

@article{shin2023accelerating,
title={Accelerating optimal power flow with {GPU}s: {SIMD} abstraction of nonlinear programs and condensed-space interior-point methods},
author={Shin, Sungho and Pacaud, Fran{\c{c}}ois and Anitescu, Mihai},
Expand Down

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