Special case for Banded Matrices in LM

Project.toml

@@ -25,14 +25,17 @@ StaticArraysCore = "1e83bf80-4336-4d27-bf5d-d5a4f845583c"
 UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
 
 [weakdeps]
+BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
 FastLevenbergMarquardt = "7a0df574-e128-4d35-8cbd-3d84502bf7ce"
 LeastSquaresOptim = "0fc2ff8b-aaa3-5acd-a817-1944a5e08891"
 
 [extensions]
+NonlinearSolveBandedMatricesExt = "BandedMatrices"
 NonlinearSolveFastLevenbergMarquardtExt = "FastLevenbergMarquardt"
 NonlinearSolveLeastSquaresOptimExt = "LeastSquaresOptim"
 
 [compat]
+BandedMatrices = "1"
 ADTypes = "0.2"
 ArrayInterface = "6.0.24, 7"
 ConcreteStructs = "0.2"
@@ -58,6 +61,7 @@ Zygote = "0.6"
 julia = "1.9"
 
 [extras]
+BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
 FastLevenbergMarquardt = "7a0df574-e128-4d35-8cbd-3d84502bf7ce"
@@ -77,4 +81,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Enzyme", "BenchmarkTools", "SafeTestsets", "Pkg", "Test", "ForwardDiff", "StaticArrays", "Symbolics", "LinearSolve", "Random", "LinearAlgebra", "Zygote", "SparseDiffTools", "NonlinearProblemLibrary", "LeastSquaresOptim", "FastLevenbergMarquardt", "NaNMath"]
+test = ["Enzyme", "BenchmarkTools", "SafeTestsets", "Pkg", "Test", "ForwardDiff", "StaticArrays", "Symbolics", "LinearSolve", "Random", "LinearAlgebra", "Zygote", "SparseDiffTools", "NonlinearProblemLibrary", "LeastSquaresOptim", "FastLevenbergMarquardt", "NaNMath", "BandedMatrices"]

ext/NonlinearSolveBandedMatricesExt.jl

@@ -0,0 +1,8 @@
+module NonlinearSolveBandedMatricesExt
+
+using BandedMatrices, LinearAlgebra, NonlinearSolve, SparseArrays
+
+# This is used if we vcat a Banded Jacobian with a Diagonal Matrix in Levenberg
+@inline NonlinearSolve._vcat(B::BandedMatrix, D::Diagonal) = vcat(sparse(B), D)
+
+end

src/levenberg.jl

@@ -208,7 +208,7 @@ function SciMLBase.__init(prob::Union{NonlinearProblem{uType, iip},
         rhs_tmp = nothing
     else
         # Preserve Types
-        mat_tmp = vcat(J, DᵀD)
+        mat_tmp = _vcat(J, DᵀD)
         fill!(mat_tmp, zero(eltype(u)))
         rhs_tmp = vcat(_vec(fu1), _vec(u))
         fill!(rhs_tmp, zero(eltype(u)))

src/utils.jl

@@ -257,14 +257,17 @@ function _try_factorize_and_check_singular!(linsolve, X)
 end
 _try_factorize_and_check_singular!(::Nothing, x) = _issingular(x), false
 
-_reshape(x, args...) = reshape(x, args...)
-_reshape(x::Number, args...) = x
+@inline _reshape(x, args...) = reshape(x, args...)
+@inline _reshape(x::Number, args...) = x
 
 @generated function _axpy!(α, x, y)
     hasmethod(axpy!, Tuple{α, x, y}) && return :(axpy!(α, x, y))
     return :(@. y += α * x)
 end
 
-_needs_square_A(_, ::Number) = true
-_needs_square_A(_, ::StaticArray) = true
-_needs_square_A(alg, _) = LinearSolve.needs_square_A(alg.linsolve)
+@inline _needs_square_A(_, ::Number) = true
+@inline _needs_square_A(_, ::StaticArray) = true
+@inline _needs_square_A(alg, _) = LinearSolve.needs_square_A(alg.linsolve)
+
+# Define special concatenation for certain Array combinations
+@inline _vcat(x, y) = vcat(x, y)

test/GPU/Project.toml

@@ -1,7 +1,9 @@
 [deps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 
 [compat]
 CUDA = "5"
+LinearSolve = "2"
 NonlinearSolve = "2"

test/gpu.jl

@@ -1,4 +1,4 @@
-using CUDA, NonlinearSolve
+using CUDA, NonlinearSolve, LinearSolve
 
 CUDA.allowscalar(false)
 

test/misc.jl

@@ -0,0 +1,7 @@
+# Miscellaneous Tests
+using BandedMatrices, LinearAlgebra, NonlinearSolve, SparseArrays, Test
+
+b = BandedMatrix(Ones(5, 5), (1, 1))
+d = Diagonal(ones(5, 5))
+
+@test NonlinearSolve._vcat(b, d) == vcat(sparse(b), d)