Multiplication instead of division in triangular eigen test (#1295)

This makes the operation simpler to compile, and reduces TTFX.
```julia
julia> using LinearAlgebra

julia> A1 = UpperTriangular([1 2; 3 4])
2×2 UpperTriangular{Int64, Matrix{Int64}}:
 1  2
 ⋅  4

julia> vals, vecs = eigen(A1);

julia> @time vecs * diagm(0 => vals) / vecs ≈ A1;
  1.858678 seconds (4.22 M allocations: 214.301 MiB, 8.50% gc time, 99.97% compilation time)

julia> @time vecs * Diagonal(vals) ≈ A1 * vecs;
  0.645123 seconds (1.25 M allocations: 62.933 MiB, 17.30% gc time, 99.97% compilation time)
```

Author: jishnub

test/testtriag.jl

@@ -291,7 +291,7 @@ function test_triangular(elty1_types)
             if !(elty1 in (BigFloat, Complex{BigFloat})) # Not handled yet
                 vals, vecs = eigen(A1)
                 if (t1 == UpperTriangular || t1 == LowerTriangular) && elty1 != Int # Cannot really handle degenerate eigen space and Int matrices will probably have repeated eigenvalues.
-                    @test vecs * diagm(0 => vals) / vecs ≈ A1 atol = sqrt(eps(float(real(one(vals[1]))))) * (opnorm(A1, Inf) * n)^2
+                    @test vecs * Diagonal(vals) ≈ A1 * vecs atol = sqrt(eps(float(real(one(vals[1]))))) * (opnorm(A1, Inf) * n)^2
                 end
             end
 
@@ -409,7 +409,7 @@ function test_triangular(elty1_types)
                 end
             end
 
-            for eltyB in (Float32, Float64, BigFloat, ComplexF32, ComplexF64, Complex{BigFloat})
+            @testset for eltyB in (Float32, Float64, BigFloat, ComplexF32, ComplexF64, Complex{BigFloat})
                 # Only test methods for the same element type and a single combination of mixed element types
                 # to avoid too much compilation
                 if !(elty1 == eltyB || elty1 ∈ (ComplexF32, Int) || eltyB ∈ (ComplexF32, Int))