diff --git a/src/generic.jl b/src/generic.jl
index 2daea22..5e7e9fe 100644
--- a/src/generic.jl
+++ b/src/generic.jl
@@ -12,7 +12,7 @@ The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
 
 * `solver`: an opaque structure [`CudssSolver`](@ref) that stores the factors of the LU decomposition.
 """
-function LinearAlgebra.lu(A::CuSparseMatrixCSR{T,Cint}) where T <: BlasFloat
+function LinearAlgebra.lu(A::CuSparseMatrixCSR{T,Cint}; check = false) where T <: BlasFloat
   n = checksquare(A)
   solver = CudssSolver(A, "G", 'F')
   x = CudssMatrix(T, n)
@@ -28,7 +28,7 @@ end
 Compute the LU factorization of a sparse matrix `A` on an NVIDIA GPU, reusing the symbolic factorization stored in `solver`.
 The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
 """
-function LinearAlgebra.lu!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}) where T <: BlasFloat
+function LinearAlgebra.lu!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}; check = false) where T <: BlasFloat
   n = checksquare(A)
   cudss_set(solver, A)
   x = CudssMatrix(T, n)
@@ -55,7 +55,7 @@ The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
 
 * `solver`: Opaque structure [`CudssSolver`](@ref) that stores the factors of the LDLᴴ decomposition.
 """
-function LinearAlgebra.ldlt(A::CuSparseMatrixCSR{T,Cint}; view::Char='F') where T <: BlasFloat
+function LinearAlgebra.ldlt(A::CuSparseMatrixCSR{T,Cint}; view::Char='F', check = false) where T <: BlasFloat
   n = checksquare(A)
   structure = T <: Real ? "S" : "H"
   solver = CudssSolver(A, structure, view)
@@ -76,7 +76,7 @@ LinearAlgebra.ldlt(A::Hermitian{T,<:CuSparseMatrixCSR{T,Cint}}) where T <: BlasF
 Compute the LDLᴴ factorization of a sparse matrix `A` on an NVIDIA GPU, reusing the symbolic factorization stored in `solver`.
 The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
 """
-function LinearAlgebra.ldlt!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}) where T <: BlasFloat
+function LinearAlgebra.ldlt!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}; check = false) where T <: BlasFloat
   n = checksquare(A)
   cudss_set(solver, A)
   x = CudssMatrix(T, n)
@@ -103,7 +103,7 @@ The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
 
 * `solver`: Opaque structure [`CudssSolver`](@ref) that stores the factors of the LLᴴ decomposition.
 """
-function LinearAlgebra.cholesky(A::CuSparseMatrixCSR{T,Cint}; view::Char='F') where T <: BlasFloat
+function LinearAlgebra.cholesky(A::CuSparseMatrixCSR{T,Cint}; view::Char='F', check = false) where T <: BlasFloat
   n = checksquare(A)
   structure = T <: Real ? "SPD" : "HPD"
   solver = CudssSolver(A, structure, view)
@@ -114,8 +114,8 @@ function LinearAlgebra.cholesky(A::CuSparseMatrixCSR{T,Cint}; view::Char='F') wh
   return solver
 end
 
-LinearAlgebra.cholesky(A::Symmetric{T,<:CuSparseMatrixCSR{T,Cint}}) where T <: BlasReal = LinearAlgebra.cholesky(A.data, view=A.uplo)
-LinearAlgebra.cholesky(A::Hermitian{T,<:CuSparseMatrixCSR{T,Cint}}) where T <: BlasFloat = LinearAlgebra.cholesky(A.data, view=A.uplo)
+LinearAlgebra.cholesky(A::Symmetric{T,<:CuSparseMatrixCSR{T,Cint}}; check = false) where T <: BlasReal = LinearAlgebra.cholesky(A.data, view=A.uplo)
+LinearAlgebra.cholesky(A::Hermitian{T,<:CuSparseMatrixCSR{T,Cint}}; check = false) where T <: BlasFloat = LinearAlgebra.cholesky(A.data, view=A.uplo)
 
 """
     solver = cholesky!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint})
@@ -123,7 +123,7 @@ LinearAlgebra.cholesky(A::Hermitian{T,<:CuSparseMatrixCSR{T,Cint}}) where T <: B
 Compute the LLᴴ factorization of a sparse matrix `A` on an NVIDIA GPU, reusing the symbolic factorization stored in `solver`.
 The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
 """
-function LinearAlgebra.cholesky!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}) where T <: BlasFloat
+function LinearAlgebra.cholesky!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}; check = false) where T <: BlasFloat
   n = checksquare(A)
   cudss_set(solver, A)
   x = CudssMatrix(T, n)