format

Author: ChrisRackauckas

src/factorization.jl

@@ -84,7 +84,8 @@ end
 function do_factorization(alg::LUFactorization, A, b, u)
     A = convert(AbstractMatrix, A)
     if A isa AbstractSparseMatrixCSC
-        return lu(SparseMatrixCSC(size(A)..., getcolptr(A), rowvals(A), nonzeros(A)), check=false)
+        return lu(SparseMatrixCSC(size(A)..., getcolptr(A), rowvals(A), nonzeros(A)),
+            check = false)
     else
         fact = lu!(A, alg.pivot, check = false)
     end

test/default_algs.jl

@@ -1,17 +1,17 @@
 using LinearSolve, LinearAlgebra, SparseArrays, Test, JET
 @test LinearSolve.defaultalg(nothing, zeros(3)).alg ===
       LinearSolve.DefaultAlgorithmChoice.GenericLUFactorization
-prob = LinearProblem(rand(3,3), rand(3))
+prob = LinearProblem(rand(3, 3), rand(3))
 solve(prob)
 
 @test LinearSolve.defaultalg(nothing, zeros(50)).alg ===
       LinearSolve.DefaultAlgorithmChoice.RFLUFactorization
-prob = LinearProblem(rand(50,50), rand(50))
+prob = LinearProblem(rand(50, 50), rand(50))
 solve(prob)
-      
+
 @test LinearSolve.defaultalg(nothing, zeros(600)).alg ===
       LinearSolve.DefaultAlgorithmChoice.GenericLUFactorization
-prob = LinearProblem(rand(600,600), rand(600))
+prob = LinearProblem(rand(600, 600), rand(600))
 solve(prob)
 
 @test LinearSolve.defaultalg(LinearAlgebra.Diagonal(zeros(5)), zeros(5)).alg ===
@@ -31,8 +31,6 @@ solve(prob)
 prob = LinearProblem(sprand(11000, 11000, 0.5), zeros(11000))
 solve(prob)
 
-
-
 @static if VERSION >= v"v1.7-"
     # Test inference 
     A = rand(4, 4)