Make NonlinearSolve.jl an optional dependency

Project.toml

@@ -41,15 +41,13 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLStyle = "d8e11817-5142-5d16-987a-aa16d5891078"
 Moshi = "2e0e35c7-a2e4-4343-998d-7ef72827ed2d"
 NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
-NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 OffsetArrays = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 OrdinaryDiffEqCore = "bbf590c4-e513-4bbe-9b18-05decba2e5d8"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 RuntimeGeneratedFunctions = "7e49a35a-f44a-4d26-94aa-eba1b4ca6b47"
-SCCNonlinearSolve = "9dfe8606-65a1-4bb3-9748-cb89d1561431"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLPublic = "431bcebd-1456-4ced-9d72-93c2757fff0b"
 SciMLStructures = "53ae85a6-f571-4167-b2af-e1d143709226"
@@ -73,7 +71,9 @@ DeepDiffs = "ab62b9b5-e342-54a8-a765-a90f495de1a6"
 FMI = "14a09403-18e3-468f-ad8a-74f8dda2d9ac"
 InfiniteOpt = "20393b10-9daf-11e9-18c9-8db751c92c57"
 LabelledArrays = "2ee39098-c373-598a-b85f-a56591580800"
+NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 Pyomo = "0e8e1daf-01b5-4eba-a626-3897743a3816"
+SCCNonlinearSolve = "9dfe8606-65a1-4bb3-9748-cb89d1561431"
 
 [extensions]
 MTKBifurcationKitExt = "BifurcationKit"
@@ -82,6 +82,7 @@ MTKDeepDiffsExt = "DeepDiffs"
 MTKFMIExt = "FMI"
 MTKInfiniteOptExt = "InfiniteOpt"
 MTKLabelledArraysExt = "LabelledArrays"
+MTKNonlinearSolveExt = ["NonlinearSolve", "SCCNonlinearSolve"]
 MTKPyomoDynamicOptExt = "Pyomo"
 
 [compat]

ext/MTKNonlinearSolveExt/MTKNonlinearSolveExt.jl

@@ -0,0 +1,10 @@
+module MTKNonlinearSolveExt
+
+using ModelingToolkit
+using NonlinearSolve
+using SCCNonlinearSolve
+
+# Export the TrustRegion algorithm for use in linearization
+ModelingToolkit._get_default_nlsolve_alg() = TrustRegion()
+
+end

src/ModelingToolkit.jl

@@ -52,8 +52,7 @@ import JuliaFormatter
 using MLStyle
 import Moshi
 using Moshi.Data: @data
-using NonlinearSolve
-import SCCNonlinearSolve
+# NonlinearSolve and SCCNonlinearSolve are now loaded via extension
 using ImplicitDiscreteSolve
 using Reexport
 using RecursiveArrayTools
@@ -379,4 +378,9 @@ PrecompileTools.@compile_workload begin
     end
 end
 
+# Default nonlinear solver algorithm - will be overridden by extension when NonlinearSolve is loaded
+function _get_default_nlsolve_alg()
+    error("NonlinearSolve.jl is required for linearization with initialization. Please load NonlinearSolve.jl to use this functionality.")
+end
+
 end # module

src/linearization.jl

@@ -1,5 +1,5 @@
 """
-    lin_fun, simplified_sys = linearization_function(sys::AbstractSystem, inputs, outputs; simplify = false, initialize = true, initialization_solver_alg = TrustRegion(), kwargs...)
+    lin_fun, simplified_sys = linearization_function(sys::AbstractSystem, inputs, outputs; simplify = false, initialize = true, initialization_solver_alg = nothing, kwargs...)
 
 Return a function that linearizes the system `sys`. The function [`linearize`](@ref) provides a higher-level and easier to use interface.
 
@@ -24,7 +24,7 @@ The `simplified_sys` has undergone [`mtkcompile`](@ref) and had any occurring in
   - `outputs`: A vector of variables that indicate the outputs of the linearized input-output model.
   - `simplify`: Apply simplification in tearing.
   - `initialize`: If true, a check is performed to ensure that the operating point is consistent (satisfies algebraic equations). If the op is not consistent, initialization is performed.
-  - `initialization_solver_alg`: A NonlinearSolve algorithm to use for solving for a feasible set of state and algebraic variables that satisfies the specified operating point.
+  - `initialization_solver_alg`: A NonlinearSolve algorithm to use for solving for a feasible set of state and algebraic variables that satisfies the specified operating point. If `nothing` (default), a default algorithm will be used when NonlinearSolve.jl is loaded.
   - `autodiff`: An `ADType` supported by DifferentiationInterface.jl to use for calculating the necessary jacobians. Defaults to using `AutoForwardDiff()`
   - `kwargs`: Are passed on to `find_solvables!`
 
@@ -39,7 +39,7 @@ function linearization_function(sys::AbstractSystem, inputs,
         op = Dict(),
         p = DiffEqBase.NullParameters(),
         zero_dummy_der = false,
-        initialization_solver_alg = TrustRegion(),
+        initialization_solver_alg = nothing,
         autodiff = AutoForwardDiff(),
         eval_expression = false, eval_module = @__MODULE__,
         warn_initialize_determined = true,
@@ -81,9 +81,16 @@ function linearization_function(sys::AbstractSystem, inputs,
     ps = parameters(sys)
     h = build_explicit_observed_function(sys, outputs; eval_expression, eval_module)
 
+    # Use default algorithm if none provided and initialization is enabled
+    actual_solver_alg = if initialization_solver_alg === nothing && initialize
+        _get_default_nlsolve_alg()
+    else
+        initialization_solver_alg
+    end
+
     initialization_kwargs = (;
         abstol = initialization_abstol, reltol = initialization_reltol,
-        nlsolve_alg = initialization_solver_alg)
+        nlsolve_alg = actual_solver_alg)
 
     p = parameter_values(prob)
     t0 = current_time(prob)