format

Author: Vaibhavdixit02

src/adtypes.jl

@@ -234,11 +234,11 @@ end
 
 function spadtype_to_spsoadtype(adtype)
     if !(adtype.dense_ad isa SciMLBase.NoAD ||
-        adtype.dense_ad isa DifferentiationInterface.SecondOrder)
+         adtype.dense_ad isa DifferentiationInterface.SecondOrder)
         soadtype = AutoSparse(
-                DifferentiationInterface.SecondOrder(adtype.dense_ad, adtype.dense_ad),
-                sparsity_detector = adtype.sparsity_detector,
-                coloring_algorithm = adtype.coloring_algorithm)
+            DifferentiationInterface.SecondOrder(adtype.dense_ad, adtype.dense_ad),
+            sparsity_detector = adtype.sparsity_detector,
+            coloring_algorithm = adtype.coloring_algorithm)
     else
         soadtype = adtype
     end
@@ -247,7 +247,7 @@ end
 
 function filled_spad(adtype)
     if adtype.sparsity_detector isa ADTypes.NoSparsityDetector &&
-        adtype.coloring_algorithm isa ADTypes.NoColoringAlgorithm
+       adtype.coloring_algorithm isa ADTypes.NoColoringAlgorithm
         adtype = AutoSparse(adtype.dense_ad; sparsity_detector = TracerSparsityDetector(),
             coloring_algorithm = GreedyColoringAlgorithm())
     elseif adtype.sparsity_detector isa ADTypes.NoSparsityDetector &&
@@ -262,7 +262,6 @@ function filled_spad(adtype)
 end
 
 function generate_sparse_adtype(adtype)
-
     if !(adtype.dense_ad isa DifferentiationInterface.SecondOrder)
         adtype = filled_spad(adtype)
         soadtype = spadtype_to_spsoadtype(adtype)

test/adtests.jl

@@ -260,9 +260,14 @@ optprob.cons_h(H3, x0)
     G2 = Array{Float64}(undef, 2)
     H2 = Array{Float64}(undef, 2, 2)
 
-    optf = OptimizationFunction(rosenbrock, DifferentiationInterface.SecondOrder(ADTypes.AutoFiniteDiff(), ADTypes.AutoReverseDiff()), cons = cons)
+    optf = OptimizationFunction(rosenbrock,
+        DifferentiationInterface.SecondOrder(
+            ADTypes.AutoFiniteDiff(), ADTypes.AutoReverseDiff()),
+        cons = cons)
     optprob = OptimizationBase.instantiate_function(
-        optf, x0, DifferentiationInterface.SecondOrder(ADTypes.AutoFiniteDiff(), ADTypes.AutoReverseDiff()),
+        optf, x0,
+        DifferentiationInterface.SecondOrder(
+            ADTypes.AutoFiniteDiff(), ADTypes.AutoReverseDiff()),
         nothing, 1, g = true, h = true, hv = true,
         cons_j = true, cons_h = true, cons_vjp = true,
         cons_jvp = true, lag_h = true)
@@ -489,9 +494,13 @@ end
     H2 = Array{Float64}(undef, 2, 2)
 
     optf = OptimizationFunction(
-        rosenbrock, DifferentiationInterface.SecondOrder(ADTypes.AutoFiniteDiff(), ADTypes.AutoReverseDiff()), cons = con2_c)
+        rosenbrock, DifferentiationInterface.SecondOrder(
+            ADTypes.AutoFiniteDiff(), ADTypes.AutoReverseDiff()),
+        cons = con2_c)
     optprob = OptimizationBase.instantiate_function(
-        optf, x0, DifferentiationInterface.SecondOrder(ADTypes.AutoFiniteDiff(), ADTypes.AutoReverseDiff()),
+        optf, x0,
+        DifferentiationInterface.SecondOrder(
+            ADTypes.AutoFiniteDiff(), ADTypes.AutoReverseDiff()),
         nothing, 2, g = true, h = true, hv = true,
         cons_j = true, cons_h = true, cons_vjp = true,
         cons_jvp = true, lag_h = true)
@@ -737,12 +746,15 @@ end
     @test lag_H ≈ lag_H_expected
     @test nnz(lag_H) == 5
 
-    optf = OptimizationFunction(sparse_objective, AutoSparse(DifferentiationInterface.SecondOrder(ADTypes.AutoForwardDiff(), ADTypes.AutoZygote())), 
+    optf = OptimizationFunction(sparse_objective,
+        AutoSparse(DifferentiationInterface.SecondOrder(
+            ADTypes.AutoForwardDiff(), ADTypes.AutoZygote())),
         cons = sparse_constraints)
 
     # Instantiate the optimization problem
     optprob = OptimizationBase.instantiate_function(optf, x0,
-    AutoSparse(DifferentiationInterface.SecondOrder(ADTypes.AutoForwardDiff(), ADTypes.AutoZygote())),
+        AutoSparse(DifferentiationInterface.SecondOrder(
+            ADTypes.AutoForwardDiff(), ADTypes.AutoZygote())),
         nothing, 2, g = true, h = true, cons_j = true, cons_h = true, lag_h = true)
     # Test gradient
     G = zeros(3)

test/matrixvalued.jl

@@ -3,10 +3,13 @@ using OptimizationBase, LinearAlgebra, ForwardDiff, Zygote, FiniteDiff,
 using Test, ReverseDiff
 
 @testset "Matrix Valued" begin
-    for adtype in [AutoForwardDiff(), SecondOrder(AutoForwardDiff(), AutoZygote()), SecondOrder(AutoForwardDiff(), AutoFiniteDiff()),
+    for adtype in [AutoForwardDiff(), SecondOrder(AutoForwardDiff(), AutoZygote()),
+        SecondOrder(AutoForwardDiff(), AutoFiniteDiff()),
         AutoSparse(AutoForwardDiff(), sparsity_detector = TracerLocalSparsityDetector()),
-        AutoSparse(SecondOrder(AutoForwardDiff(), AutoZygote()), sparsity_detector = TracerLocalSparsityDetector()),
-        AutoSparse(SecondOrder(AutoForwardDiff(), AutoFiniteDiff()), sparsity_detector = TracerLocalSparsityDetector())]
+        AutoSparse(SecondOrder(AutoForwardDiff(), AutoZygote()),
+            sparsity_detector = TracerLocalSparsityDetector()),
+        AutoSparse(SecondOrder(AutoForwardDiff(), AutoFiniteDiff()),
+            sparsity_detector = TracerLocalSparsityDetector())]
         # 1. Matrix Factorization
         @show adtype
         function matrix_factorization_objective(X, A)