Link checker issues in several markdown files

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/introduction-to-pdenlpmodels/index.md

@@ -144,7 +144,7 @@ We will use the solution found to initialize our solvers.
 
 Finally, we are ready to solve the PDE-constrained optimization problem with a targeted tolerance of `10⁻⁵`.
 In the following, we will use both Ipopt and DCI on our problem.
-We refer to the tutorial [How to solve a small optimization problem with Ipopt + NLPModels](https://jso-docs.github.io/solve-an-optimization-problem-with-ipopt/)
+We refer to the tutorial [How to solve a small optimization problem with Ipopt + NLPModels](https://jso.dev/tutorials/solve-an-optimization-problem-with-ipopt/)
 for more information on `NLPModelsIpopt`.
 
 ```julia

tutorials/introduction-to-ripqp/index.md

@@ -111,7 +111,7 @@ Generic Execution stats
 
 
 The `stats` output is a
-[GenericExecutionStats](https://jso.dev/SolverCore.jl/dev/reference/#SolverCore.GenericExecutionStats).
+[GenericExecutionStats](https://jso.dev/SolverCore.jl/dev/).
 
 It is also possible to use the package [QPSReader.jl](https://github.com/JuliaSmoothOptimizers/QPSReader.jl) in order to
 read convex quadratic problems in MPS or SIF formats: (download [QAFIRO](https://raw.githubusercontent.com/JuliaSmoothOptimizers/RipQP.jl/main/test/QAFIRO.SIF))

tutorials/introduction-to-solverbenchmark/index.md

@@ -288,7 +288,7 @@ pretty_stats(df[!, [:name, :f, :t]],
 
 
 
-See the [PrettyTables.jl documentation](https://ronisbr.github.io/PrettyTables.jl/stable/man/formatters/) for more information.
+See the [PrettyTables.jl documentation](https://ronisbr.github.io/PrettyTables.jl/stable/) for more information.
 
 When using LaTeX format, the output must be understood by LaTeX.
 By default, numerical data in the table is wrapped in inline math environments.