Update tutorials to use JSOSolvers 0.14 with Symbol-based subsolver API

- Update JSOSolvers from 0.11 to 0.14 in Project.toml files
- Update Krylov from 0.9 to 0.10 (required by JSOSolvers 0.14)
- Replace Type-based subsolver API with Symbol-based API:
  * subsolver_type = CglsSolver → subsolver = :cgls
  * subsolver_type = LsmrSolver → subsolver = :lsmr
  * etc.
- Remove Krylov.jl import (no longer needed for subsolver types)
- Remove compatibility note about JSOSolvers 0.11

This addresses issue #145 by updating to the new subsolver API
introduced in JSOSolvers 0.14.0.

Affected tutorials:
- tutorials/advanced-jsosolvers/
- tutorials/introduction-to-jsosolvers/

Tests: All package tests pass ✓

Author: arnavk23

tutorials/advanced-jsosolvers/Project.toml

@@ -8,8 +8,8 @@ SolverBenchmark = "581a75fa-a23a-52d0-a590-d6201de2218a"
 
 [compat]
 ADNLPModels = "0.7"
-JSOSolvers = "0.11"
-Krylov = "0.9"
+JSOSolvers = "0.14"
+Krylov = "0.10"
 OptimizationProblems = "0.7"
 Plots = "1"
 SolverBenchmark = "0.6"

tutorials/advanced-jsosolvers/index.jmd

@@ -8,8 +8,6 @@ author: "Tangi Migot"
 
 This tutorial showcases some advanced features of solvers in JSOSolvers.
 
-**Note**: This tutorial is compatible with JSOSolvers 0.11. The subsolver is selected using the `subsolver_type` parameter with Krylov solver types (e.g., `CglsSolver`, `LsmrSolver`).
-
 ```julia
 using JSOSolvers
 ```
@@ -49,33 +47,29 @@ JSOSolvers.tronls_allowed_subsolvers
 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/).
 
-```julia
-using Krylov
-```
-
 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
-stats = trunk(nls, subsolver_type = CglsSolver)
+stats = trunk(nls, subsolver = :cgls)
 ```
 
 The same subsolver selection pattern applies to TRON's least-squares specialization:
 
 ```julia
-stats_tron = tron(nls, subsolver_type = LsmrSolver)
+stats_tron = tron(nls, subsolver = :lsmr)
 ```
 
 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)
 )
 ```
 

tutorials/introduction-to-jsosolvers/Project.toml

@@ -12,6 +12,6 @@ SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
 ADNLPModels = "0.7"
 CSV = "0.10"
 DataFrames = "1.4"
-JSOSolvers = "0.11"
+JSOSolvers = "0.14"
 Plots = "1.38"
 SolverCore = "0.3"