Apply JuliaFormatter to fix code formatting

- Applied JuliaFormatter with SciML style guide
- Formatted 5 files

🤖 Generated by OrgMaintenanceScripts.jl

Author: SciML Bot

src/factorization.jl

@@ -861,8 +861,8 @@ patterns with “more structure”.
 !!! note
 
     By default, the SparseArrays.jl are implemented for efficiency by caching the
-    symbolic factorization. If the sparsity pattern of `A` may change between solves, set `reuse_symbolic=false`. 
-    If the pattern is assumed or known to be constant, set `reuse_symbolic=true` to avoid 
+    symbolic factorization. If the sparsity pattern of `A` may change between solves, set `reuse_symbolic=false`.
+    If the pattern is assumed or known to be constant, set `reuse_symbolic=true` to avoid
     unnecessary recomputation. To further reduce computational overhead, you can disable
     pattern checks entirely by setting `check_pattern = false`. Note that this may error
     if the sparsity pattern does change unexpectedly.
@@ -887,8 +887,8 @@ A fast sparse LU-factorization which specializes on sparsity patterns with “le
 !!! note
 
     By default, the SparseArrays.jl are implemented for efficiency by caching the
-    symbolic factorization. If the sparsity pattern of `A` may change between solves, set `reuse_symbolic=false`. 
-    If the pattern is assumed or known to be constant, set `reuse_symbolic=true` to avoid 
+    symbolic factorization. If the sparsity pattern of `A` may change between solves, set `reuse_symbolic=false`.
+    If the pattern is assumed or known to be constant, set `reuse_symbolic=true` to avoid
     unnecessary recomputation. To further reduce computational overhead, you can disable
     pattern checks entirely by setting `check_pattern = false`. Note that this may error
     if the sparsity pattern does change unexpectedly.

src/simplegmres.jl

@@ -308,7 +308,7 @@ function SciMLBase.solve!(cache::SimpleGMRESCache{false}, lincache::LinearCache)
             # [cₖ  sₖ] [ r̄ₖ.ₖ ] = [rₖ.ₖ]
             # [s̄ₖ -cₖ] [hₖ₊₁.ₖ]   [ 0  ]
             (c[inner_iter], s[inner_iter],
-            R[nr + inner_iter]) = _sym_givens(
+                R[nr + inner_iter]) = _sym_givens(
                 R[nr + inner_iter],
                 Hbis)
 

test/adjoint.jl

@@ -31,7 +31,7 @@ A = rand(n, n);
 b1 = rand(n);
 
 _ff = (x,
-y) -> f(x,
+    y) -> f(x,
     y;
     alg = LinearSolve.DefaultLinearSolver(LinearSolve.DefaultAlgorithmChoice.LUFactorization))
 _ff(copy(A), copy(b1))

test/hypretests.jl

@@ -87,7 +87,7 @@ function test_interface(alg; kw...)
 
         # Solve prob directly (without cache)
         y = solve(prob, alg; cache_kwargs..., Pl = HYPRE.BoomerAMG)
-        @test A * to_array(y.u)≈b atol=atol rtol=rtol
+        @test A*to_array(y.u)≈b atol=atol rtol=rtol
         @test y.iters > 0
         @test y.resid < rtol
 
@@ -99,7 +99,7 @@ function test_interface(alg; kw...)
         cache = y.cache
         @test cache.isfresh == cache.cacheval.isfresh_A ==
               cache.cacheval.isfresh_b == cache.cacheval.isfresh_u == false
-        @test A * to_array(y.u)≈b atol=atol rtol=rtol
+        @test A*to_array(y.u)≈b atol=atol rtol=rtol
 
         # Update A
         cache.A = A
@@ -109,7 +109,7 @@ function test_interface(alg; kw...)
         cache = y.cache
         @test cache.isfresh == cache.cacheval.isfresh_A ==
               cache.cacheval.isfresh_b == cache.cacheval.isfresh_u == false
-        @test A * to_array(y.u)≈b atol=atol rtol=rtol
+        @test A*to_array(y.u)≈b atol=atol rtol=rtol
 
         # Update b
         b2 = 2 * to_array(b)
@@ -123,7 +123,7 @@ function test_interface(alg; kw...)
         cache = y.cache
         @test cache.isfresh == cache.cacheval.isfresh_A ==
               cache.cacheval.isfresh_b == cache.cacheval.isfresh_u == false
-        @test A * to_array(y.u)≈to_array(b2) atol=atol rtol=rtol
+        @test A*to_array(y.u)≈to_array(b2) atol=atol rtol=rtol
     end
     return
 end

test/nopre/enzyme.jl

@@ -33,14 +33,14 @@ b1 = rand(n);
 db1 = zeros(n);
 
 _ff = (x,
-y) -> f(x,
+    y) -> f(x,
     y;
     alg = LinearSolve.DefaultLinearSolver(LinearSolve.DefaultAlgorithmChoice.LUFactorization))
 _ff(copy(A), copy(b1))
 
 Enzyme.autodiff(Reverse,
     (x,
-    y) -> f(x,
+        y) -> f(x,
         y;
         alg = LinearSolve.DefaultLinearSolver(LinearSolve.DefaultAlgorithmChoice.LUFactorization)),
     Duplicated(copy(A), dA),