Add conj with cost_and_gradient

Signed-off-by: 周唤海 <[email protected]>

Author: fliingelephant

src/belief.jl

@@ -80,7 +80,7 @@ function _collect_message!(vectors_out::Vector, t::AbstractArray, vectors_in::Ve
     # TODO: speed up if needed!
     code = star_code(length(vectors_in))
     cost, gradient = cost_and_gradient(code, (t, vectors_in...))
-    for (o, g) in zip(vectors_out, gradient[2:end])
+    for (o, g) in zip(vectors_out, conj.(gradient[2:end]))
         o .= g
     end
     return cost[]

src/mar.jl

@@ -78,6 +78,7 @@ probabilities of the queried variables, represented by tensors.
 function marginals(tn::TensorNetworkModel; usecuda = false, rescale = true)::Dict{Vector{Int}}
     # sometimes, the cost can overflow, then we need to rescale the tensors during contraction.
     cost, grads = cost_and_gradient(tn.code, (adapt_tensors(tn; usecuda, rescale)...,))
+    grads = conj.(grads)
     @debug "cost = $cost"
     ixs = OMEinsum.getixsv(tn.code)
     queryvars = ixs[tn.unity_tensors_idx]

test/belief.jl

@@ -46,7 +46,7 @@ end
 @testset "belief propagation" begin
     n = 5
     chi = 3
-    mps_uai = TensorInference.random_tensor_train_uai(Float64, n, chi)
+    mps_uai = TensorInference.random_tensor_train_uai(ComplexF64, n, chi)
     bp = BeliefPropgation(mps_uai)
     @test TensorInference.initial_state(bp) isa TensorInference.BPState
     state, info = belief_propagate(bp)
@@ -63,7 +63,7 @@ end
 @testset "belief propagation on circle" begin
     n = 10
     chi = 3
-    mps_uai = TensorInference.random_tensor_train_uai(Float64, n, chi; periodic=true)
+    mps_uai = TensorInference.random_tensor_train_uai(ComplexF64, n, chi; periodic=true) # FIXME: fail to converge
     bp = BeliefPropgation(mps_uai)
     @test TensorInference.initial_state(bp) isa TensorInference.BPState
     state, info = belief_propagate(bp; max_iter=100, tol=1e-6)