Automatic differentiation of pfaffian

Project.toml

@@ -1,19 +1,30 @@
 name = "SkewLinearAlgebra"
 uuid = "5c889d49-8c60-4500-9d10-5d3a22e2f4b9"
 authors = ["smataigne <[email protected]> and contributors"]
-version = "1.0"
+version = "1.0.0"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 
+[weakdeps]
+ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+
+[extensions]
+SkewLinearAlgebraChainRulesCoreExt = "ChainRulesCore"
+
 [compat]
-julia = "1.6"
 LinearAlgebra = "1.6"
+ChainRulesCore = "1"
+julia = "1.6"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
+FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 
 [targets]
-test = ["Test","Random", "SparseArrays"]
+test = ["Test", "LinearAlgebra", "Random", "SparseArrays", "ChainRulesCore", "ChainRulesTestUtils", "FiniteDifferences"]

ext/SkewLinearAlgebraChainRulesCoreExt.jl

@@ -0,0 +1,30 @@
+module SkewLinearAlgebraChainRulesCoreExt
+
+using LinearAlgebra
+using SkewLinearAlgebra
+using ChainRulesCore
+
+
+function ChainRulesCore.rrule(::Type{SkewHermitian}, val) 
+    y = SkewHermitian(val)
+    function Foo_pb(ΔFoo) 
+        if isa(ΔFoo, SkewHermitian)
+            return NoTangent(), unthunk(ΔFoo).data
+        else
+            return (NoTangent(), unthunk(ΔFoo))
+        end
+    end
+    return y, Foo_pb
+end
+
+function ChainRulesCore.rrule(::typeof(pfaffian), A::SkewHermitian)
+    Ω = pfaffian(A)
+    pfaffian_pullback(ΔΩ) = NoTangent(), SkewHermitian(rmul!(inv(A)', dot(Ω, ΔΩ))) #potentially need the 0.5 here !
+    return Ω, pfaffian_pullback
+end
+
+function ChainRulesCore.ProjectTo{<:SkewHermitian}(A::AbstractArray)
+    return skewhermitian(A)
+end
+
+end

test/chainrulestests.jl

@@ -0,0 +1,34 @@
+using ChainRulesTestUtils
+using Random
+using SkewLinearAlgebra
+using LinearAlgebra
+using FiniteDifferences
+
+ChainRulesTestUtils.rand_tangent(rng::AbstractRNG, x::SkewHermitian) = skewhermitian!(rand(rng, eltype(x), size(x)...))
+
+# Required to make finite differences behave correctly
+function FiniteDifferences.to_vec(A::SkewHermitian)
+    m = size(A, 1)
+    v = [A[i,j] for i in 2:m for j in 1:i-1]
+    function from_vec(v)
+        B = zero(A)
+        k = 1
+        for i in 2:m, j in 1:i-1
+            @inbounds B[i,j] = v[k]
+            k += 1
+        end
+        return B
+    end
+    return v, from_vec
+end
+
+@testset "automatic differentiation" begin
+    m = 10
+    inds = [1,2]
+    A = skewhermitian(rand(m, m))
+
+    test_rrule(SkewHermitian, A) # test constructor
+
+    test_rrule(pfaffian, A) # test pfaffian
+    test_rrule(pfaffian, SkewHermitian(A[inds, inds])) # test pfaffian of submatrix
+end

test/runtests.jl

@@ -519,3 +519,5 @@ end
         @test E.vectors*Diagonal(E.values)*E.vectors' ≈ B
     end
 end
+
+include("chainrulestests.jl")