diff --git a/src/CUDSS.jl b/src/CUDSS.jl
index 121ba16..682c0d9 100644
--- a/src/CUDSS.jl
+++ b/src/CUDSS.jl
@@ -6,7 +6,7 @@ using LinearAlgebra
 using SparseArrays
 
 import CUDA: @checked, libraryPropertyType, cudaDataType, initialize_context, retry_reclaim, CUstream
-import LinearAlgebra: lu, lu!, ldlt, ldlt!, cholesky, cholesky!, ldiv!, BlasFloat
+import LinearAlgebra: lu, lu!, ldlt, ldlt!, cholesky, cholesky!, ldiv!, BlasFloat, BlasReal
 
 include("libcudss.jl")
 include("error.jl")
diff --git a/src/generic.jl b/src/generic.jl
index 39c4706..cf4e28b 100644
--- a/src/generic.jl
+++ b/src/generic.jl
@@ -8,10 +8,10 @@ function LinearAlgebra.lu(A::CuSparseMatrixCSR{T}) where T <: BlasFloat
   return solver
 end
 
-function LinearAlgebra.ldlt(A::CuSparseMatrixCSR{T}) where T <: BlasFloat
+function LinearAlgebra.ldlt(A::CuSparseMatrixCSR{T}; view::Char='F') where T <: BlasFloat
   n = LinearAlgebra.checksquare(A)
   structure = T <: Real ? "S" : "H"
-  solver = CudssSolver(A, structure, 'F')
+  solver = CudssSolver(A, structure, view)
   (T <: Complex) && cudss_set(solver, "pivot_type", 'N')
   x = CudssMatrix(T, n)
   b = CudssMatrix(T, n)
@@ -20,10 +20,13 @@ function LinearAlgebra.ldlt(A::CuSparseMatrixCSR{T}) where T <: BlasFloat
   return solver
 end
 
-function LinearAlgebra.cholesky(A::CuSparseMatrixCSR{T}) where T <: BlasFloat
+LinearAlgebra.ldlt(A::Symmetric{T,<:CuSparseMatrixCSR{T}) where T <: BlasReal = LinearAlgebra.ldlt(A.data, view=A.uplo)
+LinearAlgebra.ldlt(A::Hermitian{T,<:CuSparseMatrixCSR{T}) where T <: BlasFloat = LinearAlgebra.ldlt(A.data, view=A.uplo)
+
+function LinearAlgebra.cholesky(A::CuSparseMatrixCSR{T}; view::Char='F') where T <: BlasFloat
   n = LinearAlgebra.checksquare(A)
   structure = T <: Real ? "SPD" : "HPD"
-  solver = CudssSolver(A, structure, 'F')
+  solver = CudssSolver(A, structure, view)
   x = CudssMatrix(T, n)
   b = CudssMatrix(T, n)
   cudss("analysis", solver, x, b)
@@ -31,6 +34,9 @@ function LinearAlgebra.cholesky(A::CuSparseMatrixCSR{T}) where T <: BlasFloat
   return solver
 end
 
+LinearAlgebra.cholesky(A::Symmetric{T,<:CuSparseMatrixCSR{T}) where T <: BlasReal = LinearAlgebra.cholesky(A.data, view=A.uplo)
+LinearAlgebra.cholesky(A::Hermitian{T,<:CuSparseMatrixCSR{T}) where T <: BlasFloat = LinearAlgebra.cholesky(A.data, view=A.uplo)
+
 for fun in (:lu!, :ldlt!, :cholesky!)
   @eval begin
     function LinearAlgebra.$fun(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T}) where T <: BlasFloat
diff --git a/test/test_cudss.jl b/test/test_cudss.jl
index 1836961..6227127 100644
--- a/test/test_cudss.jl
+++ b/test/test_cudss.jl
@@ -218,27 +218,32 @@ function cudss_generic()
       @test norm(r_gpu2) ≤ √eps(R)
     end
 
-    @testset "view = $view" for view in ('F',)
+    @testset "view = $view" for view in ('L', 'U', 'F')
       @testset "Symmetric -- Hermitian" begin
         A_cpu = sprand(T, n, n, 0.01) + I
         A_cpu = A_cpu + A_cpu'
         X_cpu = zeros(T, n, p)
         B_cpu = rand(T, n, p)
 
-        A_gpu = CuSparseMatrixCSR(A_cpu)
+        (view == 'L') && (A_gpu = CuSparseMatrixCSR(A_cpu |> tril))
+        (view == 'U') && (A_gpu = CuSparseMatrixCSR(A_cpu |> triu))
+        (view == 'F') && (A_gpu = CuSparseMatrixCSR(A_cpu))
         X_gpu = CuMatrix(X_cpu)
         B_gpu = CuMatrix(B_cpu)
 
-        solver = ldlt(A_gpu)
+        solver = ldlt(A_gpu; view)
         ldiv!(X_gpu, solver, B_gpu)
         R_gpu = B_gpu - A_gpu * X_gpu
         @test norm(R_gpu) ≤ √eps(R)
 
-        A_gpu2 = rand(R) * A_gpu
+        c = rand(R)
+        A_cpu2 = c * A_cpu
+        A_gpu2 = c * A_gpu
+
         ldlt!(solver, A_gpu2)
         X_gpu .= B_gpu
         ldiv!(solver, X_gpu)
-        R_gpu2 = B_gpu - A_gpu2 * X_gpu
+        R_gpu2 = B_gpu - CuSparseMatrixCSR(A_cpu2) * X_gpu
         @test norm(R_gpu2) ≤ √eps(R)
       end
 
@@ -248,20 +253,25 @@ function cudss_generic()
         X_cpu = zeros(T, n, p)
         B_cpu = rand(T, n, p)
 
-        A_gpu = CuSparseMatrixCSR(A_cpu)
+        (view == 'L') && (A_gpu = CuSparseMatrixCSR(A_cpu |> tril))
+        (view == 'U') && (A_gpu = CuSparseMatrixCSR(A_cpu |> triu))
+        (view == 'F') && (A_gpu = CuSparseMatrixCSR(A_cpu))
         X_gpu = CuMatrix(X_cpu)
         B_gpu = CuMatrix(B_cpu)
 
-        solver = cholesky(A_gpu)
+        solver = cholesky(A_gpu; view)
         ldiv!(X_gpu, solver, B_gpu)
         R_gpu = B_gpu - A_gpu * X_gpu
         @test norm(R_gpu) ≤ √eps(R)
 
-        A_gpu2 = rand(R) * A_gpu
+        c = rand(R)
+        A_cpu2 = c * A_cpu
+        A_gpu2 = c * A_gpu
+
         cholesky!(solver, A_gpu2)
         X_gpu .= B_gpu
         ldiv!(solver, X_gpu)
-        R_gpu2 = B_gpu - A_gpu2 * X_gpu
+        R_gpu2 = B_gpu - CuSparseMatrixCSR(A_cpu2) * X_gpu
         @test norm(R_gpu2) ≤ √eps(R)
       end
     end