This year's JuliaCon 2022/JuMP-dev 2022 featured three talks on JuliaSmoothOptimizers in the JuMP-dev stream.
Dominique Orban gave a complete overview in 25 minutes (!!) of the JuliaSmoothOptimizers organization. The talk mentions all the key packages to model optimization problems (NLPModels.jl & Co), solve models (JSOSolvers.jl & Co) and do simple yet amazing benchmarks with SolverBenchmark.jl.
Geoffroy Leconte presented RipQP.jl a multi-precision algorithm for convex quadratic optimization. The multi-precision feature illustrates the strength of pure Julia implementations, and ensuing performance.
Tangi Migot showed an application of the JuliaSmoothOptimizers framework to PDE-constrained optimization problems modeled with PDENLPModels.jl.
You can re-watch the three talks on Youtube: