Add tests for lmul! for LowerTriangular blockdiagonals

test/linalg.jl

@@ -78,7 +78,7 @@ end
  evals, evecs = eigen(Matrix(B))
 
  @test E isa Eigen
- @test Matrix(E) ≈ B 
+ @test Matrix(E) ≈ B
 
  # There is no test like @test eigen(B) == eigen(Matrix(B))
  # 1. this fails in the complex case. Probably a convergence thing that leads to ever so slight differences
@@ -88,7 +88,7 @@ end
  @static if VERSION < v"1.2"
  # pre-v1.2 we need to sort the eigenvalues consistently
  # Since eigenvalues may be complex here, I use this function, which works for this test.
- # This test is already somewhat fragile w. r. t. degenerate eigenvalues 
+ # This test is already somewhat fragile w. r. t. degenerate eigenvalues
  # and this just makes this a little worse.
  perm_bd = sortperm(real.(evals_bd) + 100*imag.(evals_bd))
  evals_bd = evals_bd[perm_bd]
@@ -131,7 +131,7 @@ end
  E = Eigen(block_vals, blocks(vecs)[i])
  evals_bd, evecs_bd = E
  evals, evecs = eigen(block)
- 
+
  @test block ≈ Matrix(E)
 
  @static if VERSION < v"1.2"
@@ -144,7 +144,7 @@ end
  evals = evals[perm]
  evecs = evecs[:, perm]
  end
- 
+
  @test evals_bd ≈ evals
  @test all(min.(abs.(evecs_bd - evecs), abs.(evecs_bd + evecs)) .< 1e-13)
  end
@@ -245,6 +245,21 @@ end
  end
  end
  end # SVD
+ @testset "Left multiplication" begin
+ N1 = 20
+ N2 = 8
+ N3 = 5
+ N4 = N1 + N3 - N2
+ A = BlockDiagonal([rand(rng, N1, N1), rand(rng, N2, N2)])
+ B = BlockDiagonal([rand(rng, N1, N2), rand(rng, N3, N4)])
+ x = rand(rng, N1 + N2)
+ y = rand(rng, N2 + N4)
+
+ @testset "Lower triangular" begin
+ @test lmul!(LowerTriangular(A), copy(x)) ≈ lmul!(LowerTriangular(Matrix(A)), x)
+ @test lmul!(LowerTriangular(B), copy(y)) ≈ lmul!(LowerTriangular(Matrix(B)), y)
+ end
+ end
  @testset "Left division" begin
  N1 = 20
  N2 = 8