Add absorb for putting (part of) the contents of one tensor in another

src/TensorKit.jl

@@ -76,7 +76,7 @@ export leftorth, rightorth, leftnull, rightnull,
        isposdef, isposdef!, ishermitian, sylvester, rank, cond
 export braid, braid!, permute, permute!, transpose, transpose!, twist, twist!, repartition,
        repartition!
-export catdomain, catcodomain
+export catdomain, catcodomain, embed!
 
 export OrthogonalFactorizationAlgorithm, QR, QRpos, QL, QLpos, LQ, LQpos, RQ, RQpos,
        SVD, SDD, Polar

src/spaces/gradedspace.jl

@@ -168,23 +168,19 @@ function fuse(V₁::GradedSpace{I}, V₂::GradedSpace{I}) where {I<:Sector}
 end
 
 function infimum(V₁::GradedSpace{I}, V₂::GradedSpace{I}) where {I<:Sector}
-    if V₁.dual == V₂.dual
-        typeof(V₁)(c => min(dim(V₁, c), dim(V₂, c))
-                   for c in
-                       union(sectors(V₁), sectors(V₂)), dual in V₁.dual)
-    else
+    Visdual = isdual(V₁)
+    Visdual == isdual(V₂) ||
         throw(SpaceMismatch("Infimum of space and dual space does not exist"))
-    end
+    return typeof(V₁)((Visdual ? dual(c) : c) => min(dim(V₁, c), dim(V₂, c))
+                      for c in intersect(sectors(V₁), sectors(V₂)); dual=Visdual)
 end
 
 function supremum(V₁::GradedSpace{I}, V₂::GradedSpace{I}) where {I<:Sector}
-    if V₁.dual == V₂.dual
-        typeof(V₁)(c => max(dim(V₁, c), dim(V₂, c))
-                   for c in
-                       union(sectors(V₁), sectors(V₂)), dual in V₁.dual)
-    else
+    Visdual = isdual(V₁)
+    Visdual == isdual(V₂) ||
         throw(SpaceMismatch("Supremum of space and dual space does not exist"))
-    end
+    return typeof(V₁)((Visdual ? dual(c) : c) => max(dim(V₁, c), dim(V₂, c))
+                      for c in union(sectors(V₁), sectors(V₂)); dual=Visdual)
 end
 
 function Base.show(io::IO, V::GradedSpace{I}) where {I<:Sector}

src/spaces/homspace.jl

@@ -125,6 +125,13 @@ function dim(W::HomSpace)
     return d
 end
 
+"""
+    fusiontrees(W::HomSpace)
+
+Return the fusiontrees corresponding to all valid fusion channels of a given `HomSpace`.
+"""
+fusiontrees(W::HomSpace) = fusionblockstructure(W).fusiontreelist
+
 # Operations on HomSpaces
 # -----------------------
 """

src/tensors/linalg.jl

@@ -512,6 +512,36 @@ function catcodomain(t1::TT, t2::TT) where {S,N₂,TT<:AbstractTensorMap{<:Any,S
     return t
 end
 
+"""
+    embed!(tdst::AbstactTensorMap, tsrc::AbstractTensorMap)
+
+Embed the contents of `tsrc` into `tdst`, which may have different sizes of data.
+This is equivalent to the following operation on dense arrays, but also works for symmetric
+tensors. Note also that this only overwrites the regions that are shared, and will do
+nothing on the ones that are not, so it is up to the user to properly initialize the
+destination.
+
+```julia
+sub_axes = map((x, y) -> 1:min(x, y), size(tdst), size(tsrc))
+tdst[sub_axes...] .= tsrc[sub_axes...]
+```
+"""
+function embed!(tdst::AbstractTensorMap, tsrc::AbstractTensorMap)
+    numin(tdst) == numin(tsrc) && numout(tdst) == numout(tsrc) ||
+        throw(DimensionError("Incompatible number of indices for source and destination"))
+    S = spacetype(tdst)
+    S == spacetype(tsrc) || throw(SpaceMismatch("incompatible spacetypes"))
+    dom = mapreduce(infimum, ⊗, domain(tdst), domain(tsrc); init=one(S))
+    cod = mapreduce(infimum, ⊗, codomain(tdst), codomain(tsrc); init=one(S))
+    for (f1, f2) in fusiontrees(cod ← dom)
+        @inbounds data_dst = tdst[f1, f2]
+        @inbounds data_src = tsrc[f1, f2]
+        sub_axes = map(Base.OneTo ∘ min, size(data_dst), size(data_src))
+        data_dst[sub_axes...] .= data_src[sub_axes...]
+    end
+    return tdst
+end
+
 # tensor product of tensors
 """
     ⊗(t1::AbstractTensorMap, t2::AbstractTensorMap, ...) -> TensorMap

test/tensors.jl

@@ -739,6 +739,18 @@ for V in spacelist
                 @test t ≈ t′
             end
         end
+        @timedtestset "Tensor embedding" begin
+            t1 = rand(V1 ⊕ V1, V2 ⊗ V3)
+            t2 = rand(V1, V2 ⊗ V3)
+
+            # embedding small into large
+            t3 = @constinferred embed!(zerovector(t1), t2)
+            @test norm(t3) ≈ norm(t2)
+
+            # embedding large into small
+            t4 = @constinferred embed!(zerovector(t2), t1)
+            @test norm(t4) < norm(t1)
+        end
     end
     TensorKit.empty_globalcaches!()
 end