diff --git a/tensor.cc b/tensor.cc
index e60fbab..cde2058 100644
--- a/tensor.cc
+++ b/tensor.cc
@@ -447,6 +447,7 @@ static void outputcallback(const SUPERINDEX &I, T *v)
 //print indices flat
 for(int i=0; i<I.size(); ++i)
 	for(int j=0; j<I[i].size(); ++j) *sout << I[i][j]<<" ";
+//*sout<<" "<< " "<<(void *)v<<" "<< *v<<std::endl;
 *sout<<" "<< *v<<std::endl;
 }
 
@@ -1075,6 +1076,53 @@ return r;
 }
 
 
+template<typename T> 
+NRVec<NRMat<T> > Tensor<T>::Tucker(typename LA_traits<T>::normtype thr)
+{
+int r=rank();
+NRVec<NRMat<T> > ret(r);
+if(r<2) return ret;
+
+int rr=0;	
+for(int i=0; i<shape.size(); ++i)
+	for(int j=0; j<shape[i].number; ++j) //loop over all indices
+		{
+		NRMat<T> um;
+		NRVec<indexgroup> ushape;
+		{
+		Tensor<T> u=unwind_index(i,j);
+		ushape=u.shape;
+		um=u.matrix();
+		}
+		int mini=um.nrows(); if(um.ncols()<mini) mini=um.ncols(); //compact SVD, expect descendingly sorted values
+		NRMat<T> u(um.nrows(),mini),vt(mini,um.ncols());
+		NRVec<typename LA_traits<T>::normtype> w(mini);
+		singular_decomposition(um,&u,w,&vt,0);
+		um.resize(0,0); //deallocate
+		int preserve=mini;
+		for(int k=0; k<mini; ++k) if(w[k]<thr) {preserve=k; break;}
+		if(preserve==0) laerror("singular tensor in Tucker decomposition");
+		NRMat<T> umnew;
+		if(preserve<mini)
+			{
+			vt=vt.submatrix(0,preserve-1,0,um.ncols()-1);
+			w=w.subvector(0,preserve-1);
+			umnew=u.submatrix(0,um.nrows()-1,0,preserve-1);
+			}
+		else umnew=u;
+		ret[rr++]=vt.transpose(true);
+		umnew.diagmultr(w);
+		//rebuild tensor of the preserved shape from matrix
+		ushape[0].range=preserve;
+		NRVec<T> newdata(umnew);
+		*this = Tensor(ushape,newdata);
+		}
+return ret;
+}
+
+
+
+
 
 
 template class Tensor<double>;
diff --git a/tensor.h b/tensor.h
index a3aa56b..c69bc43 100644
--- a/tensor.h
+++ b/tensor.h
@@ -1,6 +1,6 @@
 /*
     LA: linear algebra C++ interface library
-    Copyright (C) 2024 Jiri Pittner <jiri.pittner@jh-inst.cas.cz> or <jiri@pittnerovi.com>
+    Copyright (C) 2024-2025 Jiri Pittner <jiri.pittner@jh-inst.cas.cz> or <jiri@pittnerovi.com>
 
     This program is free software: you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by
@@ -21,6 +21,7 @@
 //stored in an efficient way
 //each index group has a specific symmetry (nosym,sym,antisym)
 //additional symmetry between index groups (like in 2-electron integrals) is not supported directly, you would need to nest the class to Tensor<Tensor<T> >
+//leftmost index is least significant (changing fastest) in the storage order
 //presently only a rudimentary implementation
 //presently limited to 2G data size due to NRVec - maybe use a typedef LA_index 
 //to uint64_t in the future in vector and matrix classes
@@ -36,12 +37,16 @@
 #include "smat.h"
 #include "miscfunc.h"
 
+//TODO:
+//@@@!!!!!! - implement index names  and contractions, unwinding etc. by named index list
+//
 //@@@contraction inside one tensor - compute resulting shape, loopover the shape, create index into the original tensor + loop over the contr. index, do the summation, store result
 //@@@ will need to store vector of INDEX to the original tensor for the result's flatindex
 //@@@ will not be particularly efficient
 //
-//@@@permutation of individual indices - chech the indices in sym groups remain adjacent, calculate result's shape, loopover the result and permute using unwind_callback
-//@@@todo!!! - implement index names - flat vector of names, and contraction by named index list
+//@@@?general permutation of individual indices - check the indices in sym groups remain adjacent, calculate result's shape, loopover the result and permute using unwind_callback
+//
+//
 
 namespace LA {
 
@@ -147,7 +152,9 @@ public:
 	explicit Tensor(const NRVec<T> &x);
 	explicit Tensor(const NRMat<T> &x);
 	explicit Tensor(const NRSMat<T> &x);
+	NRMat<T> matrix() const {return NRMat<T>(data,data.size()/groupsizes[0],groupsizes[0],0);}; //reinterpret as matrix with column index being the tensor's leftmost index group (typically the unwound single index)
 
+	bool is_flat() const {for(int i=0; i<shape.size(); ++i) if(shape[i].number>1) return false; return true;};
 	void clear() {data.clear();};
 	int rank() const {return myrank;};
 	int calcrank(); //is computed from shape
@@ -207,7 +214,7 @@ public:
 	void grouploopover(void (*callback)(const GROUPINDEX &, T *)); //loop over all elements disregarding the internal structure of index groups
 
 	Tensor permute_index_groups(const NRPerm<int> &p) const; //rearrange the tensor storage permuting index groups as a whole
-	Tensor unwind_index(int group, int index) const; //separate an index from a group and expand it to full range as the least significant one
+	Tensor unwind_index(int group, int index) const; //separate an index from a group and expand it to full range as the least significant one (the leftmost one)
 	Tensor unwind_indices(const INDEXLIST &il) const; //the same for a list of indices
 	void addcontraction(const Tensor &rhs1, int group, int index, const Tensor &rhs2, int rhsgroup, int rhsindex, T alpha=1, T beta=1, bool doresize=false, bool conjugate1=false, bool conjugate=false); //rhs1 will have more significant non-contracted indices in the result than rhs2
 	inline Tensor contraction(int group, int index, const Tensor &rhs, int rhsgroup, int rhsindex, T alpha=1, bool conjugate1=false, bool conjugate=false) const {Tensor<T> r; r.addcontraction(*this,group,index,rhs,rhsgroup,rhsindex,alpha,0,true, conjugate1, conjugate); return r; };
@@ -225,8 +232,8 @@ public:
 	void split_index_group(int group); //formal split of a non-symmetric index group WITHOUT the need for data reorganization
 	void merge_adjacent_index_groups(int groupfrom, int groupto); //formal merge of non-symmetric index groups  WITHOUT the need for data reorganization
 	Tensor merge_index_groups(const NRVec<int> &groups) const;
+	NRVec<NRMat<T> > Tucker(typename LA_traits<T>::normtype thr=1e-12); //HOSVD-Tucker decomposition, return core tensor in *this, flattened 
 
-	//TODO perhaps implement application of a permutation algebra to a product of several tensors
 };