Make it easier to dispatch to SVD solver methods defined

in GenericLinearAlgebra by prefixing the computational methods with
underscores.

Also make eigen and svd test includable without loading packages first

Author: andreasnoack

src/svd.jl

@@ -102,7 +102,7 @@ function svdDemmelKahan!(B::Bidiagonal{T}, n1, n2, U = nothing, Vt = nothing) wh
     return B
 end
 
-function LinearAlgebra.svdvals!(B::Bidiagonal{T}, tol = eps(T); debug = false) where T<:Real
+function _svdvals!(B::Bidiagonal{T}; tol = eps(T), debug = false) where T<:Real
 
     n = size(B, 1)
     n1 = 1
@@ -174,7 +174,7 @@ function LinearAlgebra.svdvals!(B::Bidiagonal{T}, tol = eps(T); debug = false) w
     else
         # Just transpose the matrix. Since we are only interested in the
         # values here it doesn't matter.
-        return svdvals!(Bidiagonal(d, e, :U), tol; debug = debug)
+        return svdvals!(Bidiagonal(d, e, :U); tol = tol, debug = debug)
     end
 end
 
@@ -255,4 +255,7 @@ function bidiagonalize!(A::AbstractMatrix)
     end
 end
 
-LinearAlgebra.svdvals!(A::StridedMatrix) = svdvals!(bidiagonalize!(A)[1])
+_svdvals!(A::StridedMatrix; tol = eps(T), debug = false) = svdvals!(bidiagonalize!(A)[1], tol = tol, debug = debug)
+
+LinearAlgebra.svdvals!(B::Bidiagonal{T}; tol = eps(T), debug = false) where T<:Real = _svdvals!(B, tol = tol, debug = debug)
+LinearAlgebra.svdvals!(A::StridedMatrix; tol = eps(real(eltype(A))), debug = false) = _svdvals!(A, tol = tol, debug = debug)

test/eigengeneral.jl

@@ -1,4 +1,4 @@
-using GenericLinearAlgebra
+using Test, GenericLinearAlgebra, LinearAlgebra
 
 @testset "The General eigenvalue problem" begin
 

test/svd.jl

@@ -1,9 +1,10 @@
-using Test
-using GenericLinearAlgebra
-using Quaternions
+using Test, GenericLinearAlgebra, LinearAlgebra, Quaternions
 
 @testset "Singular value decomposition" begin
-    @testset "Problem dimension ($m,$n)" for (m,n) in ((6,5), (6,6), (5,6))
+    @testset "Problem dimension ($m,$n)" for
+        (m,n) in ((6,5)     , (6,6)     , (5,6),
+                  (60, 50)  , (60, 60)  , (50, 60),
+                  (200, 150), (200, 200), (150, 200))
 
         vals = reverse(collect(1:min(m,n)))
         U = qr(Quaternion{Float64}[Quaternion(randn(4)...) for i = 1:m, j = 1:min(m,n)]).Q
@@ -20,14 +21,14 @@ using Quaternions
         # This matrix used to hang (for n = 70). Thanks to Ivan Slapničar for reporting.
         n = 70
         J = Bidiagonal(0.5 * ones(n), ones(n-1), :U)
-        @test LinearAlgebra.svdvals!(copy(J)) ≈ svdvals(J)
-        @test LinearAlgebra.svdvals!(copy(J))[end] / svdvals(J)[end] - 1 < n*eps()
+        @test GenericLinearAlgebra._svdvals!(copy(J)) ≈ svdvals(J)
+        @test GenericLinearAlgebra._svdvals!(copy(J))[end] / svdvals(J)[end] - 1 < n*eps()
     end
 
     @testset "Extending Base methods. Problem dimension" for
-        (m, n) in ((10,9), # tall
-                   (10,10),# square
-                   (9,10)) # wide
+        (m, n) in ((10,  9), # tall
+                   (10, 10), # square
+                   (9 , 10)) # wide
 
         A = randn(m,n)
         @test svdvals(A) ≈ Vector{Float64}(svdvals(big.(A)))