type stability of matrix functions

Author: araujoms

src/dense.jl

@@ -592,7 +592,7 @@ function schurpow(A::AbstractMatrix, p)
     end
 
     # if A has nonpositive real eigenvalues, retmat is a nonprincipal matrix power.
-    if isreal(retmat)
+    if eltype(A) <: Real && isreal(retmat)
         return real(retmat)
     else
         return retmat
@@ -602,20 +602,19 @@ function (^)(A::AbstractMatrix{T}, p::Real) where T
     checksquare(A)
     # Quicker return if A is diagonal
     if isdiag(A)
-        TT = promote_op(^, T, typeof(p))
-        retmat = copymutable_oftype(A, TT)
-        for i in diagind(retmat, IndexStyle(retmat))
-            retmat[i] = retmat[i] ^ p
+        if T <: Real && any(<(0), diagview(A))
+            return applydiagonal(x -> complex(x)^p, A)
+        else
+            return applydiagonal(x -> x^p, A)
         end
-        return retmat
     end
 
     # For integer powers, use power_by_squaring
     isinteger(p) && return integerpow(A, p)
 
     # If possible, use diagonalization
     if ishermitian(A)
-        return (Hermitian(A)^p)
+        return parent(Hermitian(A)^p)
     end
 
     # Otherwise, use Schur decomposition
@@ -745,7 +744,7 @@ function exp!(A::StridedMatrix{T}) where T<:BlasFloat
         end
         return A
     elseif ishermitian(A)
-        return copytri!(parent(exp(Hermitian(A))), 'U', true)
+        return parent(exp(Hermitian(A)))
     end
     ilo, ihi, scale = LAPACK.gebal!('B', A)    # modifies A
     nA   = opnorm(A, 1)
@@ -918,10 +917,10 @@ julia> log(A)
 function log(A::AbstractMatrix)
     # If possible, use diagonalization
     if isdiag(A) && eltype(A) <: Union{Real,Complex}
-        if eltype(A) <: Real && all(>=(0), diagview(A))
-            return applydiagonal(log, A)
+        if eltype(A) <: Real && any(<(0), diagview(A))
+            return applydiagonal(log ∘ complex, A)
         else
-            return applydiagonal(log∘complex, A)
+            return applydiagonal(log, A)
         end
     elseif ishermitian(A)
         logHermA = log(Hermitian(A))
@@ -1004,13 +1003,14 @@ sqrt(::AbstractMatrix)
 function sqrt(A::AbstractMatrix{T}) where {T<:Union{Real,Complex}}
     if checksquare(A) == 0
         return copy(float(A))
-    elseif isdiag(A) && (T <: Complex || all(x -> x ≥ zero(x), diagview(A)))
-        # Real Diagonal sqrt requires each diagonal element to be positive
-        return applydiagonal(sqrt, A)
+    elseif isdiag(A)
+        if T <: Real && any(<(0), diagview(A))
+            return applydiagonal(sqrt ∘ complex, A)
+        else
+            return applydiagonal(sqrt, A)
+        end
     elseif ishermitian(A)
-        sqrtHermA = sqrt(Hermitian(A))
-        PS = parent(sqrtHermA)
-        return ishermitian(sqrtHermA) ? copytri_maybe_inplace(PS, 'U', true) : PS
+        return parent(sqrt(Hermitian(A)))
     elseif istriu(A)
         return triu!(parent(sqrt(UpperTriangular(A))))
     elseif isreal(A)
@@ -1044,7 +1044,7 @@ sqrt(A::TransposeAbsMat) = transpose(sqrt(parent(A)))
 Computes the real-valued cube root of a real-valued matrix `A`. If `T = cbrt(A)`, then
 we have `T*T*T ≈ A`, see example given below.
 
-If `A` is symmetric, i.e., of type `HermOrSym{<:Real}`, then ([`eigen`](@ref)) is used to
+If `A` is real-symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to
 find the cube root. Otherwise, a specialized version of the p-th root algorithm [^S03] is
 utilized, which exploits the real-valued Schur decomposition ([`schur`](@ref))
 to compute the cube root.
@@ -1077,7 +1077,7 @@ function cbrt(A::AbstractMatrix{<:Real})
     elseif isdiag(A)
         return applydiagonal(cbrt, A)
     elseif issymmetric(A)
-        return cbrt(Symmetric(A, :U))
+        return copytri!(parent(cbrt(Symmetric(A))), 'U')
     else
         S = schur(A)
         return S.Z * _cbrt_quasi_triu!(S.T) * S.Z'
@@ -1118,7 +1118,7 @@ end
 
 Compute the matrix cosine of a square matrix `A`.
 
-If `A` is symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to
+If `A` is real-symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to
 compute the cosine. Otherwise, the cosine is determined by calling [`exp`](@ref).
 
 # Examples
@@ -1160,7 +1160,7 @@ end
 
 Compute the matrix sine of a square matrix `A`.
 
-If `A` is symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to
+If `A` is real-symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to
 compute the sine. Otherwise, the sine is determined by calling [`exp`](@ref).
 
 # Examples
@@ -1265,7 +1265,7 @@ end
 
 Compute the matrix tangent of a square matrix `A`.
 
-If `A` is symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to
+If `A` is real-symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to
 compute the tangent. Otherwise, the tangent is determined by calling [`exp`](@ref).
 
 # Examples
@@ -1357,7 +1357,7 @@ _subadd!!(X, Y) = X - Y, X + Y
 
 Compute the inverse matrix cosine of a square matrix `A`.
 
-If `A` is symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to
+If `A` is real-symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to
 compute the inverse cosine. Otherwise, the inverse cosine is determined by using
 [`log`](@ref) and [`sqrt`](@ref).  For the theory and logarithmic formulas used to compute
 this function, see [^AH16_1].
@@ -1391,7 +1391,7 @@ end
 
 Compute the inverse matrix sine of a square matrix `A`.
 
-If `A` is symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to
+If `A` is real-symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to
 compute the inverse sine. Otherwise, the inverse sine is determined by using [`log`](@ref)
 and [`sqrt`](@ref).  For the theory and logarithmic formulas used to compute this function,
 see [^AH16_2].
@@ -1425,7 +1425,7 @@ end
 
 Compute the inverse matrix tangent of a square matrix `A`.
 
-If `A` is symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to
+If `A` is real-symmetric or Hermitian, its eigendecomposition ([`eigen`](@ref)) is used to
 compute the inverse tangent. Otherwise, the inverse tangent is determined by using
 [`log`](@ref).  For the theory and logarithmic formulas used to compute this function, see
 [^AH16_3].
@@ -1436,8 +1436,8 @@ compute the inverse tangent. Otherwise, the inverse tangent is determined by usi
 ```julia-repl
 julia> atan(tan([0.5 0.1; -0.2 0.3]))
 2×2 Matrix{ComplexF64}:
-  0.5+1.38778e-17im  0.1-2.77556e-17im
- -0.2+6.93889e-17im  0.3-4.16334e-17im
+  0.5  0.1
+ -0.2  0.3
 ```
 """
 function atan(A::AbstractMatrix)
@@ -1450,7 +1450,12 @@ function atan(A::AbstractMatrix)
     SchurF = Schur{Complex}(schur(A))
     U = im * UpperTriangular(SchurF.T)
     R = triu!(parent(log((I + U) / (I - U)) / 2im))
-    return SchurF.Z * R * SchurF.Z'
+    retmat = SchurF.Z * R * SchurF.Z'
+    if eltype(A) <: Real
+        return real(retmat)
+    else
+        return retmat
+    end
 end
 
 """
@@ -1493,7 +1498,12 @@ function asinh(A::AbstractMatrix)
     SchurF = Schur{Complex}(schur(A))
     U = UpperTriangular(SchurF.T)
     R = triu!(parent(log(U + sqrt(I + U^2))))
-    return SchurF.Z * R * SchurF.Z'
+    retmat = SchurF.Z * R * SchurF.Z'
+    if eltype(A) <: Real
+        return real(retmat)
+    else
+        return retmat
+    end
 end
 
 """

src/symmetric.jl

@@ -867,7 +867,7 @@ function ^(A::SymSymTri{<:Complex}, p::Real)
     return Symmetric(schurpow(A, p))
 end
 
-for func in (:cos, :sin, :tan, :cosh, :sinh, :tanh, :atan, :asinh, :atanh, :cbrt)
+for func in (:cos, :sin, :tan, :cosh, :sinh, :tanh, :atan, :asinh, :cbrt)
     @eval begin
         function ($func)(A::SelfAdjoint)
             F = eigen(A)
@@ -890,7 +890,7 @@ function cis(A::SelfAdjoint)
     return nonhermitianwrappertype(A)(retmat)
 end
 
-for func in (:acos, :asin)
+for func in (:acos, :asin, :atanh)
     @eval begin
         function ($func)(A::SelfAdjoint)
             F = eigen(A)

src/triangular.jl

@@ -1996,7 +1996,7 @@ function powm!(A0::UpperTriangular, p::Real)
         A[i, i] = -A[i, i]
     end
     # Compute the Padé approximant
-    c = 0.5 * (p - m) / (2 * m - 1)
+    c = (p - m) / (4 * m - 2)
     triu!(A)
     S = c * A
     Stmp = similar(S)

test/dense.jl

@@ -639,8 +639,8 @@ end
         sinA1 = convert(Matrix{elty}, [0.2865568596627417 -1.107751980582015 -0.13772915374386513;
                                        -0.6227405671629401 0.2176922827908092 -0.5538759902910078;
                                        -0.6227405671629398 -0.6916051440348725 0.3554214365346742])
-        @test @inferred(cos(A1)) ≈ cosA1
-        @test @inferred(sin(A1)) ≈ sinA1
+        @test cos(A1) ≈ cosA1
+        @test sin(A1) ≈ sinA1
 
         cosA2 = convert(Matrix{elty}, [-0.6331745163802187 0.12878366262380136 -0.17304181968301532;
                                        0.12878366262380136 -0.5596234510748788 0.5210483146041339;
@@ -663,22 +663,22 @@ end
 
         # Identities
         for (i, A) in enumerate((A1, A2, A3, A4, A5))
-            @test @inferred(sincos(A)) == (sin(A), cos(A))
+            @test sincos(A) == (sin(A), cos(A))
             @test cos(A)^2 + sin(A)^2 ≈ Matrix(I, size(A))
             @test cos(A) ≈ cos(-A)
             @test sin(A) ≈ -sin(-A)
-            @test @inferred(tan(A)) ≈ sin(A) / cos(A)
+            @test tan(A) ≈ sin(A) / cos(A)
 
             @test cos(A) ≈ real(exp(im*A))
             @test sin(A) ≈ imag(exp(im*A))
             @test cos(A) ≈ real(cis(A))
             @test sin(A) ≈ imag(cis(A))
-            @test @inferred(cis(A)) ≈ cos(A) + im * sin(A)
+            @test cis(A) ≈ cos(A) + im * sin(A)
 
-            @test @inferred(cosh(A)) ≈ 0.5 * (exp(A) + exp(-A))
-            @test @inferred(sinh(A)) ≈ 0.5 * (exp(A) - exp(-A))
-            @test @inferred(cosh(A)) ≈ cosh(-A)
-            @test @inferred(sinh(A)) ≈ -sinh(-A)
+            @test cosh(A) ≈ 0.5 * (exp(A) + exp(-A))
+            @test sinh(A) ≈ 0.5 * (exp(A) - exp(-A))
+            @test cosh(A) ≈ cosh(-A)
+            @test sinh(A) ≈ -sinh(-A)
 
             # Some of the following identities fail for A3, A4 because the matrices are singular
             if i in (1, 2, 5)
@@ -687,7 +687,7 @@ end
                 @test @inferred(cot(A)) ≈ inv(tan(A))
                 @test @inferred(sech(A)) ≈ inv(cosh(A))
                 @test @inferred(csch(A)) ≈ inv(sinh(A))
-                @test @inferred(coth(A)) ≈ inv(@inferred tanh(A))
+                @test @inferred(coth(A)) ≈ inv(tanh(A))
             end
             # The following identities fail for A1, A2 due to rounding errors;
             # probably needs better algorithm for the general case
@@ -904,11 +904,6 @@ end
     end
 end
 
-@testset "matrix logarithm is type-inferable" for elty in (Float32,Float64,ComplexF32,ComplexF64)
-    A1 = randn(elty, 4, 4)
-    @inferred Union{Matrix{elty},Matrix{complex(elty)}} log(A1)
-end
-
 @testset "Additional matrix square root tests" for elty in (Float64, ComplexF64)
     A11 = convert(Matrix{elty}, [3 2; -5 -3])
     @test sqrt(A11)^2 ≈ A11

test/symmetric.jl

@@ -1199,13 +1199,64 @@ end
     end
 end
 
-@testset "asin/acos/acosh for matrix outside the real domain" begin
+@testset "asin/acos/acosh/tanh for matrix outside the real domain" begin
     M = [0 2;2 0] #eigenvalues are ±2
     for T ∈ (Float32, Float64, ComplexF32, ComplexF64)
         M2 = Hermitian(T.(M))
         @test sin(asin(M2)) ≈ M2
         @test cos(acos(M2)) ≈ M2
         @test cosh(acosh(M2)) ≈ M2
+        @test tanh(atanh(M2)) ≈ M2
+    end
+end
+
+@testset "type inference of matrix functions" begin
+    for T ∈ (Float32, Float64, ComplexF32, ComplexF64)
+        a = randn(T, 2, 2)
+        syma = Symmetric(a)
+        symtria = SymTridiagonal(syma)
+        herma = Hermitian(a)
+        #nasty functions
+        for f in (x->x^real(T)(0.3), sqrt, log, asin, acos, acosh, atanh)
+            if T <: Real
+                @test @inferred Matrix{Complex{T}} f(a) isa Matrix
+                @test @inferred Symmetric{Complex{T}} f(syma) isa Symmetric
+                @test @inferred Symmetric{Complex{T}} f(symtria) isa Symmetric
+                @test @inferred Symmetric{Complex{T}} f(herma) isa Union{Symmetric{Complex{T}}, Hermitian{T}}
+            else
+                @test @inferred f(a) isa Matrix{T}
+                @test @inferred Matrix{T} f(herma) isa Union{Matrix{T}, Hermitian{T}}
+            end
+        end
+        #nice functions
+        for f in (x->x^2, exp, cos, sin, tan, cosh, sinh, tanh, atan, asinh, cbrt)
+            if T <: Real
+                @test @inferred f(a) isa Matrix{T}
+                @test @inferred f(syma) isa Symmetric{T}
+                @test @inferred f(symtria) isa Symmetric{T}
+                @test @inferred f(herma) isa Hermitian{T}
+            else
+                f != cbrt && @test @inferred f(a) isa Matrix{T}
+                @test @inferred f(herma) isa Hermitian{T}
+            end
+        end
+        #special case cis
+        if T <: Real
+            @test @inferred cis(a) isa Matrix{Complex{T}}
+            @test @inferred cis(syma) isa Symmetric{Complex{T}}
+            @test @inferred cis(symtria) isa Symmetric{Complex{T}}
+            @test @inferred cis(herma) isa Symmetric{Complex{T}}
+        else
+            @test @inferred cis(a) isa Matrix{T}
+            @test @inferred cis(herma) isa Matrix{T}
+        end
+        #special case sincos
+        if T <: Real
+            @test @inferred sincos(syma) isa Tuple{Symmetric{T}, Symmetric{T}}
+            @test @inferred sincos(symtria) isa Tuple{Symmetric{T}, Symmetric{T}}
+        end
+        @test @inferred sincos(a) isa Tuple{Matrix{T}, Matrix{T}}
+        @test @inferred sincos(herma) isa Tuple{Hermitian{T}, Hermitian{T}}
     end
 end