Merge branch 'JuliaSmoothOptimizers:main' into youtube

Author: arnavk23

.github/workflows/LinkChecker.yml

@@ -17,7 +17,7 @@ jobs:
           ref: 'gh-pages'
 
       - name: Link Checker
-        uses: lycheeverse/lychee-action@v1.1.0
+        uses: lycheeverse/lychee-action@v2.0.2
         with:
           args: --verbose --no-progress **/*.html --exclude=".+gstatic.+" --exclude="https://doi.org.+" --exclude="https://github.com/JuliaSmoothOptimizers/PACKAGE.jl"
         env:

Project.toml

@@ -9,8 +9,8 @@ LinearOperators = "5c8ed15e-5a4c-59e4-a42b-c7e8811fb125"
 NodeJS = "2bd173c7-0d6d-553b-b6af-13a54713934c"
 
 [compat]
-Bibliography = "0.2"
+Bibliography = "0.3"
 Franklin = "0.10"
 JSON = "1"
 LinearOperators = "2"
-NodeJS = "1"
+NodeJS = "2"

_data/bibtex/articles2019.bib

@@ -7,7 +7,7 @@ @article{estrin-orban-saunders-2019c
   number = {3},
   pages = {1102--1124},
   grant = {NSERC 299010-04},
-  preprint = {https://dx.doi.org/10.13140/RG.2.2.34725.81123},
+  preprint = {https://doi.org/10.13140/RG.2.2.34725.81123},
   gerad = {G-2018-40},
   doi = {10.1137/18M1194948}
 }
@@ -21,7 +21,7 @@ @article{buttari-orban-ruiz-titley-peloquin-2019
   number = {5},
   pages = {S409--S432},
   grant = {NSERC 299010-04},
-  preprint = {https://www.gerad.ca/en/papers/G-2018-42/view},
+  preprint = {https://www.gerad.ca/en/papers/G-2018-42.pdf},
   gerad = {G-2018-42},
   doi = {10.1137/18M1194900}
 }
@@ -35,7 +35,7 @@ @article{dahito-orban-2019
   year = {2019},
   pages = {1988--2025},
   grant = {NSERC 299010-04},
-  preprint = {https://www.gerad.ca/en/papers/G-2018-50/view},
+  preprint = {https://www.gerad.ca/en/papers/G-2018-50.pdf},
   gerad = {G-2018-50},
   doi = {10.1137/18M1204255}
 }
@@ -49,7 +49,7 @@ @article{estrin-orban-saunders-2019b
   number = {1},
   pages = {254--275},
   grant = {NSERC 299010-04},
-  preprint = {https://goo.gl/uyPRk5},
+  preprint = {https://web.stanford.edu/group/SOL/software/lslq/lslq.pdf},
   gerad = {G-2017-05},
   doi = {10.1137/17M1113552}
 }

_data/bibtex/articles2020.bib

@@ -20,7 +20,7 @@ @article{orban-siqueira-2020
   volume = {76},
   number = {},
   pages = {961--989},
-  preprint = {https://www.gerad.ca/en/papers/G-2019-17/view},
+  preprint = {https://link.springer.com/article/10.1007/s10589-020-00201-2},
   doi = {10.1007/s10589-020-00201-2}
 }
 

_data/bibtex/articles2023.bib

@@ -30,7 +30,7 @@ @article{MontoisonOrban2023
 
 @article{Montoison2023,
   doi = {10.1137/21M1459265},
-  url = {https://epubs.siam.org/doi/abs/10.1137/21M1459265},
+  url = {https://doi.org/10.1137/21M1459265},
   year = {2023},
   volume = {1},
   number = {44},

_data/bibtex/articles2024.bib

@@ -14,7 +14,7 @@ @article{Migot2024
 @article{Cibulka2024,
   title = {Towards optimal spatio‐temporal decomposition of control‐related sum‐of‐squares programs},
   ISSN = {1099-1239},
-  url = {http://dx.doi.org/10.1002/rnc.7596},
+  url = {https://doi.org/10.1002/rnc.7596},
   DOI = {10.1002/rnc.7596},
   journal = {International Journal of Robust and Nonlinear Control},
   publisher = {Wiley},

_data/references.json

@@ -4,15 +4,15 @@
     "author": "Abel S. Siqueira",
     "date": "2017-12-01",
     "where": "Seminários de Análise Convexa e Otimização. UFSC, Florianópolis/SC, Brasil",
-    "link": "http://abelsiqueira.github.io/assets/2017-12-01-ufsc.pdf",
+    "link": "https://www.youtube.com/watch?v=VhAxV9I1xHs",
     "type": "talk"
   },
   {
     "title": "A Workflow for Designing Optimization Methods in the Julia Language",
     "author": "Abel S. Siqueira and Dominique Orban",
     "date": "2016-05-04",
     "where": "2016 Optimization Days. Montréal, Canada",
-    "link": "http://abelsiqueira.github.io/assets/jopt-2016.pdf",
+    "link": "https://abelsiqueira.com/blog/jopt-2016.pdf",
     "type": "talk"
   },
   {
@@ -35,7 +35,7 @@
     "author": "Abel S. Siqueira and Dominique Orban",
     "where": "Second Annual JuMP-dev Workshop. Bordeaux, France",
     "date": "2018-06-28",
-    "link": "http://abelsiqueira.github.io/assets/2018-06-28-jump-dev.pdf",
+    "link": "https://www.juliaopt.org/meetings/bordeaux2018/siqueira.pdf",
     "type": "talk"
   },
   {
@@ -50,7 +50,7 @@
     "author": "Abel S. Siqueira and Dominique Orban",
     "where": "XII Brazilian Workshop on Continuous Optimization. Iguaçu Falls, Brazil.",
     "date": "2018-07-23",
-    "link": "http://abelsiqueira.github.io/assets/2018-07-23-xiibrazopt.pdf",
+    "link": "https://abelsiqueira.com/blog/2018-07-23-xiibrazopt.pdf",
     "type": "talk"
   },
   {
@@ -66,15 +66,13 @@
     "author": "Abel S. Siqueira",
     "date": "2019-02-07",
     "where": "Tutorial: Modeling and optimization tools in Julia: An introduction to JuMP and JSO. GERAD. Montreal/QC, Canada",
-    "link": "http://abelsiqueira.github.io/assets/2019-02-07-GERAD.tar.gz",
     "type": "talk"
   },
   {
     "title": "JuliaSmoothOptimizers",
     "author": "Abel S. Siqueira",
     "date": "2019-05-31",
     "where": "Talk at PPGM - UFPR. Curitiba/PR, Brazil.",
-    "link": "http://abelsiqueira.github.io/assets/2019-05-31-PPGM.ipynb",
     "type": "talk"
   },
   {
@@ -230,7 +228,7 @@
     "author": "Alexis Montoison",
     "where": "LANS Seminar, Chicago, USA",
     "date": "2023-12-13",
-    "link": "https://github.com/JuliaSmoothOptimizers/JuliaSmoothOptimizers.github.io/files/LANS-Seminar-Argonne.pdf",
+    "link": "/files/LANS-Seminar-Argonne.pdf",
     "type": "talk"
   }
 ]

news-and-blogposts/2023/2023-12-01-jso-juliacon-eindhoven.md

@@ -8,7 +8,7 @@ As published [in a previous post](https://jso.dev/news-and-blogposts/2023/2023-0
 
 [![JuliaCon Local Eindhoven 2023](/assets/julia-packages-in-region.png)](https://juliacon.org/local/eindhoven2023/)
 
-Abel Siqueira's talk, titled [*Nonlinear Optimization with the Julia Smooth Optimizers Packages*](https://eindhoven2023.pydata.org/juliacon/talk/MYXETU/), delved into the rich ecosystem built by the JSO since its inception in 2015. With over 50 registered packages covering a wide range of nonlinear optimization and linear algebra topics, JSO has been a driving force in providing researchers with the necessary tools to seamlessly transition from prototyping to publication-ready code.
+Abel Siqueira's talk, titled [*Nonlinear Optimization with the Julia Smooth Optimizers Packages*](https://www.youtube.com/watch?v=i1eeD3uHbZ4), delved into the rich ecosystem built by the JSO since its inception in 2015. With over 50 registered packages covering a wide range of nonlinear optimization and linear algebra topics, JSO has been a driving force in providing researchers with the necessary tools to seamlessly transition from prototyping to publication-ready code.
 
 With an extensive package ecosystem and the introduction of JSOSuite, the organization continues to play a pivotal role in bridging the gap between research and practical, user-friendly solutions. As we look forward to the future, events like JuliaCon Local Eindhoven contribute significantly to the collaborative and innovative spirit of the Julia community.
 
@@ -17,4 +17,4 @@ Abstract:
 
 You can check the full talk at [The Julia Programming Language Youtube](https://www.youtube.com/watch?v=i1eeD3uHbZ4).
 
-[![Some tweets about the event](/assets/abel-juliacon-local-24.png)](https://twitter.com/eScienceCenter/status/1731659629287862537)
+[![Some tweets about the event](/assets/abel-juliacon-local-24.png)](https://x.com/eScienceCenter/status/1731659629287862537)

tutorials/advanced-jsosolvers/figures/index_11_1.png

@@ -5,16 +5,16 @@
 \preamble{Tangi Migot}
 
 
-[![OptimizationProblems 0.7.3](https://img.shields.io/badge/OptimizationProblems-0.7.3-8b0000?style=flat-square&labelColor=cb3c33)](https://jso.dev/OptimizationProblems.jl/stable/)
-[![SolverBenchmark 0.6.0](https://img.shields.io/badge/SolverBenchmark-0.6.0-006400?style=flat-square&labelColor=389826)](https://jso.dev/SolverBenchmark.jl/stable/)
-![Plots 1.39.0](https://img.shields.io/badge/Plots-1.39.0-000?style=flat-square&labelColor=999)
-[![ADNLPModels 0.7.0](https://img.shields.io/badge/ADNLPModels-0.7.0-8b0000?style=flat-square&labelColor=cb3c33)](https://jso.dev/ADNLPModels.jl/stable/)
-[![Krylov 0.9.5](https://img.shields.io/badge/Krylov-0.9.5-4b0082?style=flat-square&labelColor=9558b2)](https://jso.dev/Krylov.jl/stable/)
-[![JSOSolvers 0.11.0](https://img.shields.io/badge/JSOSolvers-0.11.0-006400?style=flat-square&labelColor=389826)](https://jso.dev/JSOSolvers.jl/stable/)
+[![OptimizationProblems 0.7.4](https://img.shields.io/badge/OptimizationProblems-0.7.4-8b0000?style=flat-square&labelColor=cb3c33)](https://jso.dev/OptimizationProblems.jl/stable/)
+[![SolverBenchmark 0.6.2](https://img.shields.io/badge/SolverBenchmark-0.6.2-006400?style=flat-square&labelColor=389826)](https://jso.dev/SolverBenchmark.jl/stable/)
+![Plots 1.41.1](https://img.shields.io/badge/Plots-1.41.1-000?style=flat-square&labelColor=999)
+[![ADNLPModels 0.7.2](https://img.shields.io/badge/ADNLPModels-0.7.2-8b0000?style=flat-square&labelColor=cb3c33)](https://jso.dev/ADNLPModels.jl/stable/)
+[![Krylov 0.10.2](https://img.shields.io/badge/Krylov-0.10.2-4b0082?style=flat-square&labelColor=9558b2)](https://jso.dev/Krylov.jl/stable/)
+[![JSOSolvers 0.14.3](https://img.shields.io/badge/JSOSolvers-0.14.3-006400?style=flat-square&labelColor=389826)](https://jso.dev/JSOSolvers.jl/stable/)
 
 
 
-# Comparing subsolvers for nonlinear least squares JSOSolvers solvers
+# Comparing subsolvers for nonlinear least squares in JSOSolvers
 
 This tutorial showcases some advanced features of solvers in JSOSolvers.
 
@@ -25,20 +25,20 @@ using JSOSolvers
 
 
 
-We benchmark different subsolvers used in the solvers TRUNK for unconstrained nonlinear least squares problems.
+We benchmark different subsolvers used in the solver TRUNK for unconstrained nonlinear least squares problems.
 The first step is to select a set of problems that are nonlinear least squares.
 
 ```julia
 using ADNLPModels
 using OptimizationProblems
 using OptimizationProblems.ADNLPProblems
 df = OptimizationProblems.meta
-names = df[(df.objtype .== :least_squares) .& (df.contype .== :unconstrained), :name]
-ad_problems = (eval(Meta.parse(problem))(use_nls = true) for problem ∈ names)
+problem_names = df[(df.objtype .== :least_squares) .& (df.contype .== :unconstrained), :name]
+ad_problems = (eval(Meta.parse(problem))(use_nls = true) for problem ∈ problem_names)
 ```
 
 ```plaintext
-Base.Generator{Vector{String}, Main.var"##WeaveSandBox#292".var"#1#2"}(Main.var"##WeaveSandBox#292".var"#1#2"(), ["arglina", "arglinb", "bard", "bdqrtic", "beale", "bennett5", "boxbod", "brownal", "br
+Base.Generator{Vector{String}, Main.var"##WeaveSandBox#277".var"#2#3"}(Main.var"##WeaveSandBox#277".var"#2#3"(), ["arglina", "arglinb", "bard", "bdqrtic", "beale", "bennett5", "boxbod", "brownal", "br
 ownbs", "brownden"  …  "power", "rat42", "rat43", "rozman1", "sbrybnd", "spmsrtls", "thurber", "tquartic", "vibrbeam", "watson"])
 ```
 
@@ -69,62 +69,78 @@ JSOSolvers.trunkls_allowed_subsolvers
 ```
 
 ```plaintext
-4-element Vector{UnionAll}:
- Krylov.CglsSolver
- Krylov.CrlsSolver
- Krylov.LsqrSolver
- Krylov.LsmrSolver
+(:cgls, :crls, :lsqr, :lsmr)
 ```
 
 
 
 
 
-This benchmark could also be followed for the solver TRON where the following subsolver are available.
+This benchmark could also be followed for the solver TRON where the following subsolvers are available.
 
 ```julia
 JSOSolvers.tronls_allowed_subsolvers
 ```
 
 ```plaintext
-4-element Vector{UnionAll}:
- Krylov.CglsSolver
- Krylov.CrlsSolver
- Krylov.LsqrSolver
- Krylov.LsmrSolver
+(:cgls, :crls, :lsqr, :lsmr)
 ```
 
 
 
 
 
 These linear least squares solvers are implemented in the package [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl).
+For detailed descriptions of each subsolver's algorithm and when to use it, see the [Krylov.jl documentation](https://jso.dev/Krylov.jl/stable/).
+
+We define a dictionary of the different solvers that will be benchmarked.
+We consider here four variants of TRUNK using the different subsolvers.
+
+For example, to call TRUNK with an explicit subsolver:
 
 ```julia
-using Krylov
+stats = trunk(nls, subsolver = :cgls)
 ```
 
+```plaintext
+"Execution stats: first-order stationary"
+```
 
 
 
-We define a dictionary of the different solvers that will be benchmarked.
-We consider here four variants of TRUNK using the different subsolvers.
+
+
+The same subsolver selection pattern applies to TRON's least-squares specialization:
+
+```julia
+stats_tron = tron(nls, subsolver = :lsmr)
+```
+
+```plaintext
+"Execution stats: first-order stationary"
+```
+
+
+
+
+
+Now we define the solver dictionary for benchmarking:
 
 ```julia
 solvers = Dict(
-  :trunk_cgls => model -> trunk(model, subsolver_type = CglsSolver),
-  :trunk_crls => model -> trunk(model, subsolver_type = CrlsSolver),
-  :trunk_lsqr => model -> trunk(model, subsolver_type = LsqrSolver),
-  :trunk_lsmr => model -> trunk(model, subsolver_type = LsmrSolver)
+  :trunk_cgls => model -> trunk(model, subsolver = :cgls),
+  :trunk_crls => model -> trunk(model, subsolver = :crls),
+  :trunk_lsqr => model -> trunk(model, subsolver = :lsqr),
+  :trunk_lsmr => model -> trunk(model, subsolver = :lsmr)
 )
 ```
 
 ```plaintext
 Dict{Symbol, Function} with 4 entries:
-  :trunk_lsqr => #5
-  :trunk_cgls => #3
-  :trunk_crls => #4
-  :trunk_lsmr => #6
+  :trunk_lsqr => #12
+  :trunk_cgls => #8
+  :trunk_crls => #10
+  :trunk_lsmr => #14
 ```
 
 
@@ -140,10 +156,10 @@ stats = bmark_solvers(solvers, ad_problems)
 
 ```plaintext
 Dict{Symbol, DataFrames.DataFrame} with 4 entries:
-  :trunk_lsqr => 66×39 DataFrame…
-  :trunk_cgls => 66×39 DataFrame…
-  :trunk_crls => 66×39 DataFrame…
-  :trunk_lsmr => 66×39 DataFrame…
+  :trunk_lsqr => 66×40 DataFrame…
+  :trunk_cgls => 66×40 DataFrame…
+  :trunk_crls => 66×40 DataFrame…
+  :trunk_lsmr => 66×40 DataFrame…
 ```
 
 
@@ -162,8 +178,8 @@ costs = [df -> .!solved(df) .* Inf .+ df.elapsed_time]
 ```
 
 ```plaintext
-1-element Vector{Main.var"##WeaveSandBox#292".var"#11#12"}:
- #11 (generic function with 1 method)
+1-element Vector{Main.var"##WeaveSandBox#277".var"#17#18"}:
+ #17 (generic function with 1 method)
 ```
 
 
@@ -180,10 +196,10 @@ gr()
 profile_solvers(stats, costs, costnames)
 ```
 
-![](figures/index_10_1.png)
+![](figures/index_11_1.png)
 
 
 
 The CRLS and CGLS variants are the ones solving more problems, and even though the difference is rather small the CGLS variant is consistently faster which seems to indicate that it is the most appropriate subsolver for TRUNK.
-The size of the problems were rather small here, so this should be confirmed on larger instance.
+The size of the problems was rather small here, so this should be confirmed on larger instances.
 Moreover, the results may vary depending on the origin of the test problems.