Add blogpost about Krylov.jl in JOSS (#189)

* Add blogpost about Krylov.jl in JOSS

Author: tmigot

news-and-blogposts/2024/2024-01-30-blogkrylov.md

@@ -0,0 +1,49 @@
+@def title = "Krylov.jl: Elevating JuliaSmoothOptimizers to New Heights!"
+@def rss_description = "The Krylov.jl paper by Alexis Montoison and Dominique Orban has been published in JOSS."
+
+# Krylov.jl: Elevating JuliaSmoothOptimizers to New Heights!
+
+Excitement is buzzing within the JuliaSmoothOptimizers community as we proudly announce a significant achievement—the publication of the [Krylov.jl paper in the Journal of Open Source Software](https://joss.theoj.org/papers/10.21105/joss.05187).
+[Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl) is not just a package; it's a success story that showcases the growing impact of our community in the world of computational mathematics.
+
+[Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl) is a powerhouse of carefully selected Krylov methods designed to tackle a diverse range of linear problems.
+Initiated by Alexis Montoison and Dominique Orban, Krylov.jl is more than a success—it's a testament to the collaborative spirit of our community.
+This work was part of Alexis' PhD work that he successfully defended this Winter ✨.
+
+## From Theory to Practice: Your Go-To Toolbox
+
+Imagine having the largest collection of Krylov processes and methods at your fingertips.
+With six processes and an impressive thirty-five methods (as of today), [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl) is breaking records and setting a new standard.
+Whether you're dealing with square systems, linear least-squares problems, or generalized saddle-point systems, this toolbox has got you covered.
+
+## Precision Unleashed: Any System, Anywhere
+
+[Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl) understands that multiprecision is crucial and supports real and complex data in any floating-point system that Julia supports.
+From single and double precision to extended precision using GNU MPFR, this toolbox adapts to your needs, ensuring accuracy in every computation.
+
+## GPU Magic: Transforming Bottlenecks into Speedways
+
+Krylov methods are renowned for their parallelizability, and [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl) takes this to the next level with seamless GPU computing support.
+Whether you're working with CUDA, ROCm, or oneAPI, our toolbox leverages the power of Julia's multiple dispatch and broadcast features, making your linear problems soar on GPUs.
+
+## Linear Operators: Redefining Efficiency
+
+In the world of high-dimensional problems, building and storing matrices can be impractical.
+[Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl) introduces the concept of linear operators, allowing you to represent Hessians and Jacobians without the computational baggage.
+It's a game-changer for nonlinear optimization, reducing computation time and memory requirements.
+
+## Performance Boost: In-Place Methods and Beyond
+
+Memory allocations slowing you down? Not with [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl)!
+All solvers come with in-place variants, minimizing those pesky allocations and deallocations. And when it comes to performance, we've got you covered—dispatching operations to BLAS routines and dynamically switching between backends for the win.
+
+## Join the Journey: Explore Krylov.jl Today
+
+Ready to unleash the power of [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl) in your projects? Dive into the documentation, explore the examples, and let the world of numerical optimization be your playground. The journey doesn't end here; it's just the beginning. [Explore Krylov.jl](https://jso.dev/Krylov.jl/stable/) today!
+
+## Community Power: Thank You!
+
+As we celebrate this success, we're not just looking back; we're looking ahead.
+[Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl) isn't just a toolbox; it's a promise—a promise of continued innovation, exploration, and pushing the boundaries of what's possible in Julia.
+
+*Cheers to JuliaSmoothOptimizers and the Success of Krylov.jl!* 🚀✨