Add definition of det

Project.toml

@@ -1,6 +1,6 @@
 name = "PDMats"
 uuid = "90014a1f-27ba-587c-ab20-58faa44d9150"
-version = "0.11.8"
+version = "0.11.9"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"

src/pdiagmat.jl

@@ -62,6 +62,7 @@ Base.kron(A::PDiagMat, B::PDiagMat) = PDiagMat( vcat([A.diag[i] * B.diag for i i
 ### Algebra
 
 Base.inv(a::PDiagMat) = PDiagMat(map(inv, a.diag))
+LinearAlgebra.det(a::PDiagMat) = prod(a.diag)
 function LinearAlgebra.logdet(a::PDiagMat)
     diag = a.diag
     return isempty(diag) ? zero(log(zero(eltype(diag)))) : sum(log, diag)

src/pdmat.jl

@@ -53,6 +53,7 @@ end
 ### Algebra
 
 Base.inv(a::PDMat) = PDMat(inv(a.chol))
+LinearAlgebra.det(a::PDMat) = det(a.chol)
 LinearAlgebra.logdet(a::PDMat) = logdet(a.chol)
 LinearAlgebra.eigmax(a::PDMat) = eigmax(a.mat)
 LinearAlgebra.eigmin(a::PDMat) = eigmin(a.mat)

src/pdsparsemat.jl

@@ -51,6 +51,7 @@ end
 ### Algebra
 
 Base.inv(a::PDSparseMat{T}) where {T<:Real} = PDMat(inv(a.mat))
+LinearAlgebra.det(a::PDSparseMat) = det(a.chol)
 LinearAlgebra.logdet(a::PDSparseMat) = logdet(a.chol)
 
 ### whiten and unwhiten

src/scalmat.jl

@@ -61,6 +61,7 @@ Base.kron(A::ScalMat, B::ScalMat) = ScalMat( dim(A) * dim(B), A.value * B.value
 ### Algebra
 
 Base.inv(a::ScalMat) = ScalMat(a.dim, inv(a.value))
+LinearAlgebra.det(a::ScalMat) = a.value^a.dim
 LinearAlgebra.logdet(a::ScalMat) = a.dim * log(a.value)
 LinearAlgebra.eigmax(a::ScalMat) = a.value
 LinearAlgebra.eigmin(a::ScalMat) = a.value

test/testutils.jl

@@ -17,6 +17,7 @@ function test_pdmat(C::AbstractPDMat, Cmat::Matrix;
                     t_cholesky::Bool=true,      # whether to test cholesky method
                     t_scale::Bool=true,         # whether to test scaling
                     t_add::Bool=true,           # whether to test pdadd
+		    t_det::Bool=true,           # whether to test det method
                     t_logdet::Bool=true,        # whether to test logdet method
                     t_eig::Bool=true,           # whether to test eigmax and eigmin
                     t_mul::Bool=true,           # whether to test multiplication
@@ -36,6 +37,7 @@ function test_pdmat(C::AbstractPDMat, Cmat::Matrix;
     isa(C, PDMatType) && t_cholesky && pdtest_cholesky(C, Cmat, cmat_eq, verbose)
     t_scale && pdtest_scale(C, Cmat, verbose)
     t_add && pdtest_add(C, Cmat, verbose)
+    t_det && pdtest_det(C, Cmat, verbose)
     t_logdet && pdtest_logdet(C, Cmat, verbose)
 
     t_eig && pdtest_eig(C, Cmat, verbose)
@@ -154,12 +156,24 @@ function pdtest_add(C::AbstractPDMat, Cmat::Matrix, verbose::Int)
     @test Mr ≈ R
 end
 
+function pdtest_det(C::AbstractPDMat, Cmat::Matrix, verbose::Int)
+    _pdt(verbose, "det")
+    @test det(C) ≈ det(Cmat)
+
+    # generic fallback in LinearAlgebra performs LU decomposition
+    if C isa Union{PDMat,PDiagMat,ScalMat}
+	@test iszero(@allocated det(C))
+    end
+end
 
 function pdtest_logdet(C::AbstractPDMat, Cmat::Matrix, verbose::Int)
     _pdt(verbose, "logdet")
-    # default tolerance in isapprox is different on 0.4. rtol argument can be deleted
-    # ≈ form used when 0.4 is no longer supported
-    @test isapprox(logdet(C), logdet(Cmat), rtol=sqrt(max(eps(real(eltype(C))), eps(real(eltype(Cmat))))))
+    @test logdet(C) ≈ logdet(Cmat)
+
+    # generic fallback in LinearAlgebra performs LU decomposition
+    if C isa Union{PDMat,PDiagMat,ScalMat}
+	@test iszero(@allocated logdet(C))
+    end
 end