tensor: bugfix in Tucker

CLAUDE.md

@@ -97,7 +97,7 @@ decisions and plans agreed with the author, so that they survive a fresh clone.
   dependent defaults live in LA_precision<T> (la_traits.h, only double and float; double values unchanged). optimize done
   2026-11-05: own trait opt_precision<T> in optimize.h, INSTANTIZE(float), float tests in testoptimize.cc (test_float);
   float LM on badly scaled problems needs my_scaling=true. reg/regsurf, csrmat, strassen, CUDA stay double-only.
-  Open: Tensor Tucker bugs (complex roundtrip, inverseorder=true abort, also double; bugtests/tensor_tucker_bug.cc).
+  Tensor Tucker bugs fixed 2026-11-06 (complex: factor = vt.transpose(false); unnamed tensors with inverseorder; bugtests/tensor/tucker.cc).
   BLAS-based NRVec members are meant for floating types only, not integers.
 - NRSMat: possible bool member `hermitian` toggling hermitian vs complex symmetric behaviour
   (Tensor(const NRSMat&) must then respect it).

doc/tensor.tex

@@ -291,7 +291,7 @@ Names are permuted together with the indices unless stated otherwise.
 \begin{lareference}
 \lafunc{void apply_permutation_algebra(const Tensor &rhs, const PermutationAlgebra<int,T> &pa, bool inverse=false, T alpha=1, T beta=0, bool conjugate_by_parity=false)}{$\mathtt{*this} \leftarrow \beta\,\mathtt{*this}$, then for all stored elements $I$ of \code{*this}: $\mathtt{this}(I) \mathrel{+}= \alpha \sum_P c_P\, \mathtt{rhs}(P(I))$, with $P$ acting on the flat index list (inverse permutations if \code{inverse}); with \code{conjugate_by_parity} odd permutations take the complex conjugate. Can represent general (anti)symmetrizers, e.g.\ \code{general_antisymmetrizer} (chapter~\ref{ch:permutation}). \code{*this} may have a symmetry which \code{rhs} lacks. The names of \code{*this} are taken from (or checked against) the names of \code{rhs} permuted by the first permutation. Element-by-element, not optimally efficient.}
 \lafunc{void apply_permutation_algebra(const NRVec<Tensor> &rhsvec, const PermutationAlgebra<int,T> &pa, bool inverse=false, T alpha=1, T beta=0)}{the same with \code{rhs} being the outer product of the tensors in \code{rhsvec}, which is not formed explicitly (e.g.\ Grassmann products building a density matrix from cumulants); rather inefficient.}
-\lafunc{NRVec<NRMat<T> > Tucker(typename LA_traits<T>::normtype thr=LA_precision<normtype>::svd_threshold(), bool inverseorder=false)}{HOSVD/Tucker decomposition: \code{*this} is replaced by the flat core tensor and the factor matrices (one per index, columns orthonormal) are returned; singular values below \code{thr} are dropped, which reduces the corresponding range of the core.}
+\lafunc{NRVec<NRMat<T> > Tucker(typename LA_traits<T>::normtype thr=LA_precision<normtype>::svd_threshold(), bool inverseorder=false)}{HOSVD/Tucker decomposition: \code{*this} is replaced by the flat core tensor and the factor matrices (one per index, columns orthonormal) are returned; singular values below \code{thr} are dropped, which reduces the corresponding range of the core. The tensor is $X=G\times_1U_1\cdots\times_rU_r$ with the core $G$ and the factor matrices $U_k$, contracted without complex conjugation ($G=X\times_1U_1^\dagger\cdots\times_rU_r^\dagger$).}
 \lafunc{Tensor inverseTucker(const NRVec<NRMat<T> > &x, bool inverseorder=false) const}{rebuild the tensor from the core (\code{*this}) and the matrices returned by \code{Tucker} with the same \code{inverseorder}.}
 \lafunc{Tensor linear_transform(const NRVec<NRMat<T> > &x) const}{transform every index of group $g$ by the matrix \code{x[g]} (\code{x[g].ncols()} must equal the range; the new range is \code{x[g].nrows()}), preserving the group symmetries: $t'_{\dots a \dots} = \sum_i x_{ai}\, t_{\dots i\dots}$.}
 \lafunc{Tensor linear_transform(const int g, const Tensor<T> &x) const; Tensor linear_transform(const int g, const NRMat<T> &x) const}{transform only group \code{g}, keeping its position and symmetry; \code{x} must be a flat rank-2 tensor (useful for raising/lowering indices) or a matrix.}

tensor.cc

@@ -1829,7 +1829,7 @@ for(int i=0; i<r; ++i)
 		umnew=u.submatrix(0,umnr-1,0,preserve-1);
 		}
 	else umnew=u;
-	ret[r-i-1]=vt.transpose(true);
+	ret[r-i-1]=vt.transpose(false);
 	umnew.diagmultr(w);
 	//rebuild tensor of the preserved shape from matrix
 	ushape.copyonwrite();
@@ -1845,7 +1845,7 @@ if(!is_flat()) laerror("this should not happen");
 if(inverseorder)
 	{
 	NRPerm<int> p(rank()); p.identity();
-	names=names_saved.permuted(p.reverse());
+	if(names_saved.size()) names=names_saved.permuted(p.reverse());
 	}
 else 
 	names=names_saved;
@@ -1889,7 +1889,7 @@ for(int i=rank()-1; i>=0; --i)
 if(inverseorder)
         {
         NRPerm<int> p(rank()); p.identity();
-        r.names=names.permuted(p.reverse());
+        if(is_named()) r.names=names.permuted(p.reverse());
         }
 else
         r.names=names;