Update tensorsfactorizations.jl

Co-Authored-By: Pietro Monticone <38562595+pitmonticone@users.noreply.github.com>
Co-Authored-By: Claudio Moroni <43729990+ClaudMor@users.noreply.github.com>

Author: 3 people

src/tensorsfactorizations.jl

@@ -1,48 +1,6 @@
-#= # This script has been copy-pasted from https://github.com/mhauru/TensorFactorizations.jl
+# This script has been copy-pasted from https://github.com/mhauru/TensorFactorizations.jl
 
-"""
-    tensorsplit(A, a, b; kwargs...)
-
-Calls tensorsvd with the arguments given to it to decompose the given tensor
-A with indices a on one side and indices b on the other.  It then splits
-the diagonal matrix of singular values into two with a square root and
-multiplies these weights into the isometric tensors.  Thus tensorsplit ends
-up splitting A into two parts, which are then returned, possibly together
-with auxiliary data such as a truncation error. If the keyword argument
-hermitian=true, an eigenvalue decomposition is used in stead of an SVD. All
-the keyword arguments are passed to either tensorsvd or tensoreig.
-
-See tensorsvd and tensoreig for further documentation.
-"""
-function tensorsplit(args...; kwargs...)
-    # Find the keyword argument hermitian.
-    # TODO This is awful, why do I have to do this?
-    hermitian = false
-    for (key, value) in kwargs
-        key == :hermitian && (hermitian = value)
-    end
 
-    if hermitian
-        res = tensoreig(args...; kwargs...)
-        S, U = res[1:2]
-        Vt_perm = [ndims(U), (1:(ndims(U) - 1))...]
-        Vt = conj!(tensorcopy(U, collect(1:ndims(U)), Vt_perm))
-        S = Diagonal(S)
-        if !isposdef(S)
-            S = complex.(S)
-        end
-        auxdata = res[3:end]
-    else
-        res = tensorsvd(args...; kwargs...)
-        U, S, Vt = res[1:3]
-        S = Diagonal(S)
-        auxdata = res[4:end]
-    end
-    S_sqrt = sqrt.(S)
-    A1 = tensorcontract(U, (1:(ndims(U) - 1)..., :a), S_sqrt, (:a, :b))
-    A2 = tensorcontract(S_sqrt, (:b, :a), Vt, (:a, 1:(ndims(Vt) - 1)...))
-    return A1, A2, auxdata...
-end =#
 
 """
     tensoreig(A, a, b; chis=nothing, eps=0,
@@ -120,90 +78,7 @@ function tensoreig(
     return retval
 end
 
-#= """
-    tensorsvd(A, a, b;
-              chis=nothing, eps=0,
-              return_error=false, print_error=false,
-              break_degenerate=false, degeneracy_eps=1e-6,
-              norm_type=:frobenius)
-
-Singular valued decomposes a tensor A. The indices of A are
-permuted so that the indices listed in the Array/Tuple a are on the "left"
-side and indices listed in b are on the "right".  The resulting tensor is
-then reshaped to a matrix, and this matrix is SVDed into U*diagm(S)*Vt.
-Finally, the unitary matrices U and Vt are reshaped to tensors so that
-they have a new index coming from the SVD, for U as the last index and for
-Vt as the first, and U has indices a as its first indices and V has
-indices b as its last indices.
-
-If eps>0 then the SVD may be truncated if the relative error can be kept
-below eps. For this purpose different dimensions to truncate to can be tried,
-and these dimensions should be listed in chis. If chis is nothing (the
-default) then the full range of possible dimensions is tried. If
-break_degenerate=false (the default) then the truncation never cuts between
-degenerate singular values. degeneracy_eps controls how close the values need
-to be to be considered degenerate.
-
-norm_type specifies the norm used to measure the error. This defaults to
-:frobenius, which means that the error measured is the Frobenius norm of the
-difference between A and the decomposition, divided by the Frobenius norm of
-A.  This is the same thing as the 2-norm of the singular values that are
-truncated out, divided by the 2-norm of all the singular values. The other
-option is :trace, in which case a 1-norm is used instead.
-
-If print_error=true the truncation error is printed. The default is false.
-
-If return_error=true then the truncation error is also returned.
-The default is false.
 
-Note that no iterative techniques are used, which means choosing to truncate
-provides no performance benefits: The full SVD is computed in any case.
-
-Output is U, S, Vt, and possibly error. Here S is a vector of
-singular values and U and Vt are isometric tensors (unitary if the matrix
-that is SVDed is square and there is no truncation) such that  U*diag(S)*Vt =
-A, up to truncation errors.
-"""
-function tensorsvd(
-    A,
-    a,
-    b;
-    chis=nothing,
-    eps=0,
-    return_error=false,
-    print_error=false,
-    break_degenerate=false,
-    degeneracy_eps=1e-6,
-    norm_type=:frobenius,
-)
-    # Create the matrix and SVD it.
-    A, shp_a, shp_b = to_matrix(A, a, b; return_tensor_shape=true)
-    fact = svd(A)
-    U, S, Vt = fact.U, fact.S, fact.Vt
-
-    # Find the dimensions to truncate to and the error caused in doing so.
-    chi, error = find_trunc_dim(S, chis, eps, break_degenerate, degeneracy_eps, norm_type)
-    # Truncate
-    S = S[1:chi]
-    U = U[:, 1:chi]
-    Vt = Vt[1:chi, :]
-
-    if print_error
-        println("Relative truncation error ($norm_type norm) in SVD: $error")
-    end
-
-    # Reshape U and V to tensors with shapes matching the shape of A and
-    # return.
-    dim = size(S)[1]
-    U_tens = reshape(U, shp_a..., dim)
-    Vt_tens = reshape(Vt, dim, shp_b...)
-    retval = (U_tens, S, Vt_tens)
-    if return_error
-        retval = (retval..., error)
-    end
-    return retval
-end
- =#
 """
 Format the bond dimensions listed in chis to a standard format.
 """