Don't try cholesky for Hermitian in factorize (#54775)

JuliaLang · Jun 13, 2024 · 1505b07 · 1505b07
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diff --git a/stdlib/LinearAlgebra/src/dense.jl b/stdlib/LinearAlgebra/src/dense.jl
@@ -1387,15 +1387,14 @@ end
     factorize(A)
 
 Compute a convenient factorization of `A`, based upon the type of the input matrix.
-`factorize` checks `A` to see if it is symmetric/triangular/etc. if `A` is passed
-as a generic matrix. `factorize` checks every element of `A` to verify/rule out
-each property. It will short-circuit as soon as it can rule out symmetry/triangular
-structure. The return value can be reused for efficient solving of multiple
-systems. For example: `A=factorize(A); x=A\\b; y=A\\C`.
+If `A` is passed as a generic matrix, `factorize` checks to see if it is
+symmetric/triangular/etc. To this end, `factorize` may check every element of `A` to
+verify/rule out each property. It will short-circuit as soon as it can rule out
+symmetry/triangular structure. The return value can be reused for efficient solving
+of multiple systems. For example: `A=factorize(A); x=A\\b; y=A\\C`.
 
 | Properties of `A`          | type of factorization                          |
 |:---------------------------|:-----------------------------------------------|
-| Positive-definite          | Cholesky (see [`cholesky`](@ref))  |
 | Dense Symmetric/Hermitian  | Bunch-Kaufman (see [`bunchkaufman`](@ref)) |
 | Sparse Symmetric/Hermitian | LDLt (see [`ldlt`](@ref))      |
 | Triangular                 | Triangular                                     |
@@ -1406,9 +1405,6 @@ systems. For example: `A=factorize(A); x=A\\b; y=A\\C`.
 | General square             | LU (see [`lu`](@ref))            |
 | General non-square         | QR (see [`qr`](@ref))            |
 
-If `factorize` is called on a Hermitian positive-definite matrix, for instance, then `factorize`
-will return a Cholesky factorization.
-
 # Examples
 ```jldoctest
 julia> A = Array(Bidiagonal(fill(1.0, (5, 5)), :U))
@@ -1427,8 +1423,9 @@ julia> factorize(A) # factorize will check to see that A is already factorized
   ⋅    ⋅    ⋅   1.0  1.0
   ⋅    ⋅    ⋅    ⋅   1.0
 ```
-This returns a `5×5 Bidiagonal{Float64}`, which can now be passed to other linear algebra functions
-(e.g. eigensolvers) which will use specialized methods for `Bidiagonal` types.
+
+This returns a `5×5 Bidiagonal{Float64}`, which can now be passed to other linear algebra
+functions (e.g. eigensolvers) which will use specialized methods for `Bidiagonal` types.
 """
 function factorize(A::AbstractMatrix{T}) where T
     m, n = size(A)
@@ -1490,12 +1487,7 @@ function factorize(A::AbstractMatrix{T}) where T
             return UpperTriangular(A)
         end
         if herm
-            cf = cholesky(A; check = false)
-            if cf.info == 0
-                return cf
-            else
-                return factorize(Hermitian(A))
-            end
+            return factorize(Hermitian(A))
         end
         if sym
             return factorize(Symmetric(A))

diff --git a/stdlib/LinearAlgebra/test/cholesky.jl b/stdlib/LinearAlgebra/test/cholesky.jl
@@ -61,16 +61,15 @@ end
 
         # Test of symmetric pos. def. strided matrix
         apd  = a'*a
-        @inferred cholesky(apd)
-        capd  = factorize(apd)
+        capd  = @inferred cholesky(apd)
         r     = capd.U
         κ     = cond(apd, 1) #condition number
 
         unary_ops_tests(apd, capd, ε*κ*n)
         if eltya != Int
             @test Factorization{eltya}(capd) === capd
             if eltya <: Real
-                @test Array(Factorization{complex(eltya)}(capd)) ≈ Array(factorize(complex(apd)))
+                @test Array(Factorization{complex(eltya)}(capd)) ≈ Array(cholesky(complex(apd)))
                 @test eltype(Factorization{complex(eltya)}(capd)) == complex(eltya)
             end
         end
@@ -358,7 +357,7 @@ end
         0.11192755545114 - 0.1603741874112385im 0.8439562576196216 + 1.0850814110398734im
         -1.0568488936791578 - 0.06025820467086475im 0.12696236014017806 - 0.09853584666755086im]
     cholesky(Hermitian(apd, :L), RowMaximum()) \ b
-    r = factorize(apd).U
+    r = cholesky(apd).U
     E = abs.(apd - r'*r)
     ε = eps(abs(float(one(ComplexF32))))
     n = 10