Simplify test setup for linalg.jl

Author: nickrobinson251

test/linalg.jl

@@ -7,20 +7,12 @@ using Test
 @testset "linalg.jl" begin
     rng = MersenneTwister(123456)
     N1, N2, N3 = 3, 4, 5
-    N = N1 + N2 + N3
     blocks1 = [rand(rng, N1, N1), rand(rng, N2, N2), rand(rng, N3, N3)]
     blocks2 = [rand(rng, N1, N1), rand(rng, N3, N3), rand(rng, N2, N2)]
-    blocks3 = [rand(rng, N1, N1), rand(rng, N2, N2), rand(rng, N2, N2)]
-
-    @testset "$T" for (T, (b1, b2, b3)) in (
-        Tuple => (BlockDiagonal(Tuple(blocks1)), BlockDiagonal(Tuple(blocks2)), BlockDiagonal(Tuple(blocks3))),
-        Vector => (BlockDiagonal(blocks1), BlockDiagonal(blocks2), BlockDiagonal(blocks3)),
-    )
-    A = rand(rng, N, N + N1)
-    B = rand(rng, N + N1, N + N2)
-    A′, B′ = A', B'
-    a = rand(rng, N)
-    b = rand(rng, N + N1)
+
+    @testset for V in (Tuple, Vector)
+    b1 = BlockDiagonal(V(blocks1))
+    b2 = BlockDiagonal(V(blocks2))
 
     @testset "mul!" begin
         c = similar(b1)
@@ -60,7 +52,7 @@ using Test
 
         @testset "eigvals on LinearAlgebra types" begin
             # `eigvals` has different methods for different types, e.g. Hermitian
-            b_herm = BlockDiagonal(T(Hermitian(rand(rng, 3, 3) + I) for _ in 1:3))
+            b_herm = BlockDiagonal(V([Hermitian(rand(rng, 3, 3) + I) for _ in 1:3]))
             @test eigvals(b_herm) ≈ eigvals(Matrix(b_herm))
             @test eigvals(b_herm, 1.0, 2.0) ≈ eigvals(Hermitian(Matrix(b_herm)), 1.0, 2.0)
         end
@@ -80,21 +72,21 @@ using Test
               0.0 1.0  5.0
               0.0 0.0  3.0]
 
-        B = BlockDiagonal(T([X, X]))
+        B = BlockDiagonal(V([X, X]))
         C = cholesky(B)
         @test C isa Cholesky{Float64, <:BlockDiagonal{Float64}}
         @test C.U ≈ cholesky(Matrix(B)).U
-        @test C.U ≈ BlockDiagonal(T([U, U]))
-        @test C.L ≈ BlockDiagonal(T([U', U']))
+        @test C.U ≈ BlockDiagonal(V([U, U]))
+        @test C.L ≈ BlockDiagonal(V([U', U']))
         @test C.UL ≈ C.U
         @test C.uplo === 'U'
         @test C.info == 0
 
         M = BlockDiagonal(map(Matrix, blocks(C.L)))
         C = Cholesky(M, 'L', 0)
         @test C.U ≈ cholesky(Matrix(B)).U
-        @test C.U ≈ BlockDiagonal(T([U, U]))
-        @test C.L ≈ BlockDiagonal(T([U', U']))
+        @test C.U ≈ BlockDiagonal(V([U, U]))
+        @test C.L ≈ BlockDiagonal(V([U', U']))
         @test C.UL ≈ C.L
         @test C.uplo === 'L'
         @test C.info == 0
@@ -104,7 +96,7 @@ using Test
         X = [  4  12 -16
               12  37 -43
              -16 -43  98]
-        B = BlockDiagonal(T([X, X]))
+        B = BlockDiagonal(V([X, X]))
 
         @testset "full=$full" for full in (true, false)