tensor: bugfix in unwind_index and linear_transform of single group

Makefile.am

@@ -1,5 +1,5 @@
 lib_LTLIBRARIES = libla.la
-include_HEADERS = LA_version.h tensor.h miscfunc.h simple.h vecmat3.h quaternion.h fortran.h cuda_la.h auxstorage.h  davidson.h   laerror.h    mat.h          qsort.h             vec.h bisection.h   diis.h       la.h         noncblas.h     smat.h numbers.h bitvector.h   fourindex.h  la_traits.h la_random.h  nonclass.h     sparsemat.h sparsesmat.h csrmat.h conjgrad.h    gmres.h      matexp.h     permutation.h   polynomial.h contfrac.h graph.h reg.h regsurf.h
+include_HEADERS = LA_version.h tensor.h miscfunc.h simple.h vecmat3.h quaternion.h fortran.h cuda_la.h auxstorage.h  davidson.h lanczos.h  laerror.h    mat.h          qsort.h             vec.h bisection.h   diis.h       la.h         noncblas.h     smat.h numbers.h bitvector.h   fourindex.h  la_traits.h la_random.h  nonclass.h     sparsemat.h sparsesmat.h csrmat.h conjgrad.h    gmres.h      matexp.h     permutation.h   polynomial.h contfrac.h graph.h reg.h regsurf.h
 libla_la_SOURCES = simple.cc tensor.cc miscfunc.cc quaternion.cc vecmat3.cc vec.cc mat.cc smat.cc sparsemat.cc sparsesmat.cc csrmat.cc laerror.cc noncblas.cc  numbers.cc bitvector.cc strassen.cc nonclass.cc cuda_la.cc fourindex.cc permutation.cc polynomial.cc contfrac.cc graph.cc la_random.cc reg.cc regsurf.cc
 nodist_libla_la_SOURCES = version.cc
 check_PROGRAMS = t test tX test_reg test_regsurf

davidson.h

@@ -33,12 +33,13 @@ namespace LA {
 //Note that for efficiency in a direct CI case the diagonalof() should cache its result
 
 
-//@@@should work for complex hermitian-only too, but was not tested yet (maybe somwhere complex conjugation will have to be added)
+//works for complex hermitian-only too, but does not converge so well for more than 1 root
 //@@@ for large krylov spaces >200 it can occur 'convergence problem in sygv/syev in diagonalize()'
 //@@@options: left eigenvectors by matrix transpose, overridesymmetric (for nrmat)
 //@@@small matrix gdiagonalize - shift complex roots up (option to gdiagonalize?)
 //@@@test gdiagonalize whether it sorts the roots and what for complex ones
 //@@@implement left eigenvectors for nonsymmetric case
+//@@@implement start from larger Krylov space than a single vector - useful for multiroot solutions
 
 
 //Davidson algorithm: J. Comp. Phys. 17:817 (1975) 
@@ -48,7 +49,7 @@ template <typename T, typename Matrix>
 extern void davidson(const Matrix &bigmat, NRVec<T> &eivals, NRVec<T> *eivecs, const char *eivecsfile, 
 		int nroots=1,  const bool verbose=0, const double eps=1e-6,
 	 	const bool incore=1, int maxit=100, const int maxkrylov = 500,
-		void (*initguess)(NRVec<T> &)=NULL)
+		void (*initguess)(NRVec<T> &)=NULL, const typename LA_traits<T>::normtype *target=NULL)
 {
 bool flag=0;
 int n=bigmat.nrows();
@@ -155,15 +156,40 @@ else
 	for(int i=0; i<=krylovsize; ++i) {r[i]/=LA_traits<T>::realpart(beta[i]); ri[i]/=LA_traits<T>::realpart(beta[i]);}
 	}
 
+if(target) //resort eigenpairs by distance from the target
+	{
+	NRVec<typename LA_traits<T>::normtype> key(krylovsize+1);
+	for(int i=0; i<=krylovsize; ++i) key[i] = abs(r[i] - *target);
+	NRPerm<int> p(krylovsize+1);
+	key.sort(0,p);
+	
+	NRVec<typename LA_traits<T>::normtype> rp(maxkrylov);
+	NRMat<T> smallVp(maxkrylov,maxkrylov);
+	for(int i=0; i<=krylovsize; ++i)
+		{
+		rp[i]= r[p[i+1]-1];
+		for(int j=0; j<=krylovsize; ++j) smallVp(j,i) = smallV(j,p[i+1]-1);
+		}
+	r = rp;
+	smallV = smallVp;
+	}
+
 typename LA_traits<T>::normtype eival_n=r[nroot];
 oldnroot=nroot;
 typename LA_traits<T>::normtype test=std::abs(smallV(krylovsize,nroot));
+//std::cout <<"NROOT = "<<nroot<<" TEST = "<<test<<std::endl;
+if(test>100.)
+	{
+	std::cout <<"Divergence in Davidson\n";
+	flag=1;
+	goto finished;
+	}
 if(test<eps) nroot++;
 if(it==0) nroot = 0;
 for(int iroot=0; iroot<=std::min(krylovsize,nroots-1); ++iroot)
 	{
         test = std::abs(smallV(krylovsize,iroot));
-        if(test>eps) nroot=std::min(nroot,iroot);
+        if(test>eps) nroot=std::min(nroot,iroot); //return to previous root
         if(verbose && iroot<=std::max(oldnroot,nroot)) 
 		{
 		std::cout <<"Davidson: iter="<<it <<" dim="<<krylovsize<<" root="<<iroot<<" eigenvalue="<<r[iroot]<<"\n";
@@ -229,7 +255,8 @@ for(j=0; j<=krylovsize; ++j)
 	typename LA_traits<T>::normtype vnorm2;
         if(!incore) s0->get(vec2,j);
 	do	{
-          	T ab = vec1*(incore?v0[j]:vec2) /smallS(j,j);
+		 // For COMPLEX the order matters, the first one gets conjuagted, changed vec1 to the right!
+          	T ab = (incore?v0[j]:vec2)*vec1 /smallS(j,j);
             	vec1.axpy(-ab,incore?v0[j]:vec2);
 		vnorm2 = vec1.norm();
 		if(vnorm2==0) 

fourindex.h

@@ -133,7 +133,7 @@ typedef enum {
 	T2ijab_unitary=7, 
 	T2IjAb_unitary=8, 
 	antisymtwoelectronrealdiracAB=9,rdm2AB=9,twoelectroncomplexmullikan_without_conjugation=9,
-	twoelectronrealmullikanreducedsymAA=10,
+	twoelectronrealmullikanreducedsymAA=10, rdm2SS=10,
 	twoelectronrealmullikanreducedsymAB=11,
 	twoelectroncomplexmullikan=12,
 } fourindexsymtype; //only permutation-nonequivalent elements are stored
@@ -155,7 +155,7 @@ static const int fourindex_permutations[fourindex_n_symmetrytypes][max_fourindex
 		{{0,1,2,3,1},{2,3,0,1,-1}}, //T2IjAb_unitary (antihermitian exponent)
 		{{0,1,2,3,1},{2,3,0,1,1}}, //antisymtwoelectronrealdiracAB
 		{{0,1,2,3,1},{2,3,0,1,1},{1,0,3,2,1},{3,2,1,0,1}}, //twoelectronrealmullikanreducedsymAA (e.g. unitary downfolded CC eff. integrals)
-		{{0,1,2,3,1},{1,0,3,2,1}}, //twoelectronrealmullikanreducedsymAB (e.g. unitary downfolded CC eff. integrals)
+		{{0,1,2,3,1},{1,0,3,2,1}}, //twoelectronrealmullikanreducedsymAB (e.g. unitary downfolded CC eff. integrals, or RDM2 spin-summed)
 		{{0,1,2,3,1},{2,3,0,1,1},{1,0,3,2,2},{3,2,1,0,2}}, //twoelectroncomplexmullikan ... 2 means complex conjugation
 		};
 
@@ -1244,7 +1244,7 @@ if (!NRSMat<T>::v) laerror("access to unallocated fourindex_dense");
 #endif
 return (*this).NRSMat<T>::operator() ((j-1)*nnbas+i-1,(b-1)*nnbas+a-1);
 }
-
+	inline void set(unsigned int i, unsigned int j, unsigned int k, unsigned int l, T elem) {this->operator()(i,j,k,l)=elem;};
 	void print(std::ostream &out) const
 		{
 		unsigned int i,j,a,b;
@@ -1385,7 +1385,12 @@ if(i<j) elem = -elem;
 if(k<l) elem = -elem;
 int I = ASMat_index_1(i,j);
 int J = ASMat_index_1(k,l);
-if (I<0 || J<0) laerror("assignment to nonexisting element");
+if (I<0 || J<0) 
+#ifdef FORBID_NONEXISTENT_ASSIGNMENTS
+	laerror("assignment to nonexisting element");
+#else
+	return;
+#endif
 #ifdef DEBUG
 if (I>=NRSMat<T>::nn || J>=NRSMat<T>::nn) laerror("index out of range");
 if (!NRSMat<T>::v) laerror("access to unallocated fourindex_dense");
@@ -1401,7 +1406,12 @@ if(i<j) elem = -elem;
 if(k<l) elem = -elem;
 int I = ASMat_index_1(i,j);
 int J = ASMat_index_1(k,l);
-if (I<0 || J<0) laerror("assignment to nonexisting element");
+if (I<0 || J<0) 
+#ifdef FORBID_NONEXISTENT_ASSIGNMENTS
+	laerror("assignment to nonexisting element");
+#else
+	return;
+#endif
 #ifdef DEBUG
 if (I>=NRSMat<T>::nn || J>=NRSMat<T>::nn) laerror("index out of range");
 if (!NRSMat<T>::v) laerror("access to unallocated fourindex_dense");

get_git_version

@@ -1,4 +1,4 @@
-#!/bin/csh -f
+#!/bin/bash
 cat <<!
 #include "LA_version.h"
 const char LA::version[] = 

la.h

@@ -28,6 +28,7 @@
 #include "bitvector.h"
 #include "conjgrad.h"
 #include "davidson.h"
+#include "lanczos.h"
 #include "diis.h"
 #include "fourindex.h"
 #include "gmres.h"

la_traits.h

@@ -392,10 +392,10 @@ static inline bool bigger(const  X<C> &x, const X<C> &y) {return x>y;} \
 static inline bool smaller(const  X<C> &x, const X<C> &y) {return x<y;} \
 static inline normtype norm (const X<C> &x) {return x.norm();} \
 static inline void axpy (X<C>&s, const X<C> &x, const C c) {s.axpy(c,x);} \
-static void put(int fd, const X<C> &x, bool dimensions=1, bool transp=0) {x.put(fd,dimensions,transp);} \
+static void put(int fd, const X<C> &x, bool dimensions=1, bool transp=0, bool orcaformat=false) {x.put(fd,dimensions,transp,orcaformat);} \
-static void get(int fd, X<C> &x, bool dimensions=1, bool transp=0) {x.get(fd,dimensions,transp);} \
+static void get(int fd, X<C> &x, bool dimensions=1, bool transp=0, bool orcaformat=false) {x.get(fd,dimensions,transp,orcaformat);} \
-static void multiput(size_t n,int fd, const X<C> *x, bool dimensions=1) {for(size_t i=0; i<n; ++i) x[i].put(fd,dimensions);} \
+static void multiput(size_t n,int fd, const X<C> *x, bool dimensions=1, bool orcaformat=false) {for(size_t i=0; i<n; ++i) x[i].put(fd,dimensions,false,orcaformat);} \
-static void multiget(size_t n,int fd, X<C> *x, bool dimensions=1) {for(size_t i=0; i<n; ++i) x[i].get(fd,dimensions);} \
+static void multiget(size_t n,int fd, X<C> *x, bool dimensions=1, bool orcaformat=false) {for(size_t i=0; i<n; ++i) x[i].get(fd,dimensions,false,orcaformat);} \
 static void copy(X<C> *dest, X<C> *src, size_t n) {for(size_t i=0; i<n; ++i) dest[i]=src[i];} \
 static void clear(X<C> *dest, size_t n) {for(size_t i=0; i<n; ++i) dest[i].clear();}\
 static void copyonwrite(X<C> &x) {x.copyonwrite();}\
@@ -435,10 +435,10 @@ static inline bool bigger(const  C &x, const C &y) {return x>y;} \
 static inline bool smaller(const  C &x, const C &y) {return x<y;} \
 static inline normtype norm (const X<C> &x) {return x.norm();}  \
 static inline void axpy (X<C>&s, const X<C> &x, const C c) {s.axpy(c,x);}  \
-static void put(int fd, const X<C> &x, bool dimensions=1, bool transp=0) {x.put(fd,dimensions);}  \
+static void put(int fd, const X<C> &x, bool dimensions=1, bool transp=0, bool orcaformat=false) {x.put(fd,dimensions,false,orcaformat);}  \
-static void get(int fd, X<C> &x, bool dimensions=1, bool transp=0) {x.get(fd,dimensions);}  \
+static void get(int fd, X<C> &x, bool dimensions=1, bool transp=0, bool orcaformat=false) {x.get(fd,dimensions,false,orcaformat);}  \
-static void multiput(size_t n,int fd, const X<C> *x, bool dimensions=1) {for(size_t i=0; i<n; ++i) x[i].put(fd,dimensions);}  \
+static void multiput(size_t n,int fd, const X<C> *x, bool dimensions=1, bool orcaformat=false) {for(size_t i=0; i<n; ++i) x[i].put(fd,dimensions,false,orcaformat);}  \
-static void multiget(size_t n,int fd, X<C> *x, bool dimensions=1) {for(size_t i=0; i<n; ++i) x[i].get(fd,dimensions);}  \
+static void multiget(size_t n,int fd, X<C> *x, bool dimensions=1, bool orcaformat=false) {for(size_t i=0; i<n; ++i) x[i].get(fd,dimensions,false,orcaformat);}  \
 static void copy(C *dest, C *src, size_t n) {for(size_t i=0; i<n; ++i) dest[i]=src[i];}  \
 static void clear(C *dest, size_t n) {for(size_t i=0; i<n; ++i) dest[i].clear();} \
 static void copyonwrite(X<C> &x) {x.copyonwrite();} \

lanczos.h

@@ -0,0 +1,212 @@
+/*
+    LA: linear algebra C++ interface library
+    Copyright (C) 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
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+*/
+#ifndef _lanczos_h
+#define _lanczos_h
+#include "vec.h"
+#include "smat.h"
+#include "mat.h"
+#include "sparsemat.h"
+#include "nonclass.h"
+#include "auxstorage.h"
+
+//TODO:
+//@@@implement restart when Krylov space is exceeded
+//@@@implement inital guess of more than 1 vector (also in davidson) and block version of the methods
+
+namespace LA {
+
+//Lanczos diagonalization of hermitean matrix 
+//matrix can be any class which has nrows(), ncols(), diagonalof(), issymmetric(), and gemv() available
+//does not even have to be explicitly stored - direct CI
+//therefore the whole implementation must be a template in a header
+
+
+
+template <typename T, typename Matrix>
+extern void lanczos(const Matrix &bigmat, NRVec<T> &eivals, NRVec<T> *eivecs, const char *eivecsfile, 
+		int nroots=1,  const bool verbose=0, const double eps=1e-6,
+	 	const bool incore=1, int maxit=100, const int maxkrylov = 1000,
+		void (*initguess)(NRVec<T> &)=NULL, const typename LA_traits<T>::normtype *target=NULL)
+{
+if(!bigmat.issymmetric()) laerror("lanczos only for hermitean matrices");
+bool flag=0;
+int n=bigmat.nrows();
+if ( n!= (int)bigmat.ncols()) laerror("non-square matrix in lanczos");
+if(eivals.size()<nroots) laerror("too small eivals dimension in lanczos");
+
+NRVec<T> vec1(n),vec2(n);
+NRVec<T> *v0,*v1;
+AuxStorage<T> *s0,*s1;
+
+if(incore)
+	{
+	v0 = new NRVec<T>[maxkrylov];
+	}
+else
+	{
+	s0 = new AuxStorage<T>;
+	}
+
+int i,j;
+
+if(nroots>=maxkrylov) nroots =maxkrylov-1;
+int  nroot=0;
+int oldnroot;
+
+
+//default guess based on lowest diagonal element of the matrix
+if(initguess) initguess(vec1);
+else
+	{
+	const T *diagonal = bigmat.diagonalof(vec2,false,true);
+	typename LA_traits<T>::normtype t=1e100; int i,j;  
+	vec1=0;
+	for(i=0, j= -1; i<n; ++i) if(LA_traits<T>::realpart(diagonal[i])<t) {t=LA_traits<T>::realpart(diagonal[i]); j=i;}
+	vec1[j]=1;
+	}
+
+NRVec<typename LA_traits<T>::normtype> alpha(maxkrylov),beta(maxkrylov-1);
+
+//initial step
+vec1.normalize();
+if(incore) v0[0]=vec1; else s0->put(vec1,0); 
+bigmat.gemv(0,vec2,'n',1,vec1); //avoid bigmat.operator*(vec), since that needs to allocate another n-sized vector
+{
+T tmp=vec2*vec1;
+alpha[0]= LA_traits<T>::realpart(tmp);
+if(LA_traits<T>::imagpart(tmp)>1e-6) laerror("matrix probably not hermitian in lanczos");
+}
+vec2.axpy(-alpha[0],vec1); 
+
+NRVec<typename LA_traits<T>::normtype> r(1); r[0]=alpha[0];
+NRVec<typename LA_traits<T>::normtype> rold;
+NRMat<typename LA_traits<T>::normtype> smallV;
+int krylovsize=1;
+//iterative Lanczos
+int it=0;
+for(j=1; j<maxkrylov;++j)
+	{
+	++it;		
+	if(it>maxit) {std::cout <<"too many interations in lanczos\n"; flag=1; goto finished;}
+	++krylovsize;
+	//extend the Krylov space (last w vector expected in vec2, last v vector in vec1)
+	beta[j-1] = vec2.norm();
+	if(beta[j-1] > 0)
+		{
+		vec2 /= beta[j-1];
+		}
+	else
+		{
+		laerror("zero norm in lanczos");
+		//could generate an arbitrary vector and orthonormalize it
+		}
+	if(incore) v0[j]=vec2; else s0->put(vec2,j);
+	vec1 *= -beta[j-1];
+	bigmat.gemv(1,vec1,'n',1,vec2);
+	{
+	T tmp=vec1*vec2;
+	alpha[j]= LA_traits<T>::realpart(tmp);
+	if(LA_traits<T>::imagpart(tmp)>1e-6) laerror("matrix probably not hermitian in lanczos");
+	}
+	vec1.axpy(-alpha[j],vec2);
+	vec1.swap(vec2); //move new w vector to vec2, new v vector to vec1
+
+	//diagonalize the tridiagonal
+	smallV.resize(j+1,j+1);
+	rold=r;
+	r=alpha.subvector(0,j);
+	NRVec<typename LA_traits<T>::normtype> rr=beta.subvector(0,j-1);
+	diagonalize3(r,rr,&smallV); 
+	if(target) //resort eigenpairs by distance from the target
+		{
+		NRVec<typename LA_traits<T>::normtype> key(j+1);
+		for(int i=0; i<=j; ++i) key[i] = abs(r[i] - *target);
+		NRPerm<int> p(j+1);
+		key.sort(0,p);
+	
+		NRVec<typename LA_traits<T>::normtype> rp(j+1);
+		NRMat<typename LA_traits<T>::normtype> smallVp(j+1,j+1);
+		for(int i=0; i<=j; ++i)
+			{
+			rp[i]= r[p[i+1]-1];
+			for(int k=0; k<=j; ++k) smallVp(k,i) = smallV(k,p[i+1]-1);
+			}
+		r = rp;
+		smallV = smallVp;
+		}
+
+       	  if(verbose)
+      	    {
+	    for(int iroot=0; iroot<std::min(krylovsize,nroots); ++iroot)
+       	         {
+       	         std::cout <<"Lanczos: iter="<<it <<" dim="<<krylovsize<<" root="<<iroot<<" eigenvalue="<<r[iroot]<<"\n";
+       	         }
+       	   std::cout.flush();
+       	   }
+	//convergence test when we have enough roots even in rold
+	if(krylovsize>nroots)
+		{
+		bool conv=true;
+		for(int  iroot=0; iroot<nroots; ++iroot)
+			if(abs(r[iroot]-rold[iroot])>eps) conv=false;
+		if(conv)
+			{
+			flag=0;
+			goto converged;
+			}
+		}
+	}
+flag=1;
+goto finished;
+
+converged:
+AuxStorage<typename LA_traits<T>::elementtype> *ev;
+if(eivecsfile) ev = new AuxStorage<typename LA_traits<T>::elementtype>(eivecsfile);
+if(verbose) {std::cout << "Lanczos converged in "<<it<<" iterations.\n"; std::cout.flush();}
+for(nroot=0; nroot<nroots; ++nroot)
+	{
+        eivals[nroot]=r[nroot];
+	if(eivecs)
+		{
+		vec1=0;
+		for(j=0; j<krylovsize; ++j )
+			{
+            		if(!incore) s0->get(vec2,j);
+			vec1.axpy(smallV(j,nroot),incore?v0[j]:vec2);
+			}
+		vec1.normalize();
+        	if(eivecs) eivecs[nroot]|=vec1;
+		if(eivecsfile)
+			{
+			ev->put(vec1,nroot);
+			}
+		}
+	}
+
+if(eivecsfile) delete ev;
+
+
+finished:
+if(incore) {delete[] v0;}
+else  {delete s0;}
+
+if(flag) laerror("no convergence in lanczos");
+}
+
+}//namespace
+#endif

mat.cc

@@ -121,12 +121,12 @@ const NRVec<T> NRMat<T>::row(const int i, int l) const {
  * @see NRVec<T>::put()
  ******************************************************************************/
 template <typename T>
-void NRMat<T>::put(int fd, bool dim, bool transp) const {
+void NRMat<T>::put(int fd, bool dim, bool transp, bool orcaformat) const {
 #ifdef CUDALA
 	if(location != cpu) {
 		NRMat<T> tmp = *this;
 		tmp.moveto(cpu);
-		tmp.put(fd, dim, transp);
+		tmp.put(fd, dim, transp, orcaformat);
 		return;
 	}
 #endif
@@ -134,6 +134,11 @@ void NRMat<T>::put(int fd, bool dim, bool transp) const {
 	if(dim){
 		if(sizeof(int) != write(fd,&(transp?mm:nn),sizeof(int))) laerror("write failed");
 		if(sizeof(int) != write(fd,&(transp?nn:mm),sizeof(int))) laerror("write failed");
+		if(orcaformat)
+			{
+			int tmp=sizeof(T);
+			if(sizeof(int) != write(fd,&tmp,sizeof(int))) laerror("write failed");
+			}
 	}
 
 	if(transp){  //not particularly efficient
@@ -167,12 +172,12 @@ void NRMat<T>::put(int fd, bool dim, bool transp) const {
  * @see NRVec<T>::get(), copyonwrite()
  ******************************************************************************/
 template <typename T>
-void NRMat<T>::get(int fd, bool dim, bool transp){
+void NRMat<T>::get(int fd, bool dim, bool transp, bool orcaformat){
 #ifdef CUDALA
 	if(location != cpu){
 		NRMat<T> tmp;
 		tmp.moveto(cpu);
-		tmp.get(fd, dim, transp);
+		tmp.get(fd, dim, transp,orcaformat);
 		tmp.moveto(getlocation());
 		*this = tmp;
 		return;
@@ -183,6 +188,12 @@ void NRMat<T>::get(int fd, bool dim, bool transp){
 	if(dim){
 		if(sizeof(int) != read(fd, &nn0, sizeof(int))) laerror("read failed");
 		if(sizeof(int) != read(fd, &mm0, sizeof(int))) laerror("read failed");
+		if(orcaformat)
+			{
+			int tmp;
+			if(sizeof(int) != read(fd, &tmp, sizeof(int))) laerror("read failed");
+			if(tmp!=sizeof(T)) laerror("mismatch in orca format");
+			}
 		if(transp) resize(mm0, nn0); else resize(nn0, mm0);
 	}else{
 		copyonwrite();

mat.h

@@ -305,9 +305,9 @@ public:
 	inline size_t size() const;
 
 	//! unformatted input
-        void get(int fd, bool dimensions = 1, bool transposed = false);
+        void get(int fd, bool dimensions = 1, bool transposed = false, bool orcaformat=false);
 	//! unformatted output
-        void put(int fd, bool dimensions = 1, bool transposed = false) const;
+        void put(int fd, bool dimensions = 1, bool transposed = false, bool orcaformat=false) const;
 	//! formatted output
 	void fprintf(FILE *f, const char *format, const int modulo) const;
 	//! formatted input

miscfunc.cc

@@ -767,7 +767,7 @@ return(bern[n]);
 //
 //enlarge storage size if needed
 //
-#define SIMPLICIAL_MAXD 8
+#define SIMPLICIAL_MAXD 16
 #define SIMPLICIAL_MAXN 1024
 
 unsigned long long simplicial(int d, int n)
@@ -783,7 +783,7 @@ if(d==1) return n;
 //resize precomputed part as needed
 if(n>maxn)
 	{
-	if(n>=SIMPLICIAL_MAXN) laerror("storage overflow in simplicial");
+	if(n>=SIMPLICIAL_MAXN) laerror("storage overflow in simplicial, increase MAXN");
 	int newdim=2*n; 
 	if(newdim<2*maxn) newdim=2*maxn;
 	if(newdim>=SIMPLICIAL_MAXN) newdim=SIMPLICIAL_MAXN-1;
@@ -797,7 +797,7 @@ if(n>maxn)
 
 if(d>maxd)
 	{
-	if(d>=SIMPLICIAL_MAXD) laerror("storage overflow in simplicial");
+	if(d>=SIMPLICIAL_MAXD) laerror("storage overflow in simplicial, increase MAXD");
 	for(int dd=maxd+1; dd<=d; ++dd)
 		{
 		stored[dd-2][0]=0;
@@ -817,7 +817,7 @@ int inverse_simplicial(int d,  unsigned long long s)
 if(s==0) return 0;
 if(d==0 || s==1) return 1;
 if(d==1) return (int)s;
-if(d>=SIMPLICIAL_MAXD) laerror("storage overflow in inverse_simplicial");
+if(d>=SIMPLICIAL_MAXD) laerror("storage overflow in inverse_simplicial - increase MAXD");
 
 static int maxd=0;
 static Polynomial<double> polynomials[SIMPLICIAL_MAXD];

nonclass.cc

@@ -944,7 +944,31 @@ extern "C" void FORNAME(zggev)(const char *JOBVL, const char *JOBVR, const FINT
                 std::complex<double> *VL, const FINT *LDVL,  std::complex<double> *VR, const FINT *LDVR,
                 std::complex<double> *WORK, const FINT *LWORK, double *RWORK, FINT *INFO );
 
+//tridiagonal
+extern "C" void FORNAME(dsteqr)(const char *compz, const  FINT *n, double *d, double *d1, double *z, const FINT *ldz,  double *work, FINT *info);
 
+void diagonalize3(NRVec<double> &d, NRVec<double> &d1, NRMat<double> *v, const bool corder, int n0)
+{
+	FINT n = n0? n0: d.size();
+	if(d1.size()<n-1) laerror("inconsistent dimensions in diagonalize3");
+	if(v) {if(n!=v->nrows()||n!=v->ncols()) laerror("inconsistent dimensions in diagonalize3");}
+	d.copyonwrite();
+	d1.copyonwrite();
+	if(v) v->copyonwrite();
+	
+	FINT ldz=n;
+	FINT info;
+	int worksize=2*n-2;
+	if(worksize<1) worksize=1;
+        double *work = new double[worksize];
+	char job=v?'i':'n';
+	FORNAME(dsteqr)(&job,&n,&d[0],&d1[0],(v?&(*v)(0,0):NULL),&ldz,work,&info);
+	delete[] work;
+	if (v && corder) v->transposeme();
+
+	if (info < 0) laerror("illegal argument in dsteqr() fo diagonalize3");
+	if (info > 0) laerror("convergence problem in dsteqr() fo diagonalize3");
+}
 
 
 //statics for sorting

nonclass.h

@@ -68,22 +68,43 @@ return positive_power(z,i);
 
 
 template <class T>
-const NRSMat<T> twoside_transform(const NRSMat<T> &S, const NRMat<T> &C, bool transp=0) //calculate C^dagger S C
+const NRSMat<T> twoside_transform(const NRSMat<T> &S, const NRMat<T> &C, bool transp=0) //calculate C^dagger S C for symmetric/hermitian S
 {
+char tchar='t';
+if(LA_traits<T>::is_complex()) tchar='c';
 if(transp)
 	{
 	NRMat<T> tmp =  C * S;
 	NRMat<T> result(C.nrows(),C.nrows());
-	result.gemm((T)0,tmp,'n',C,'t',(T)1);
+	result.gemm((T)0,tmp,'n',C,tchar,(T)1);
 	return NRSMat<T>(result);
 	}
 NRMat<T> tmp = S * C;
 NRMat<T> result(C.ncols(),C.ncols());
-result.gemm((T)0,C,'t',tmp,'n',(T)1);
+result.gemm((T)0,C,tchar,tmp,'n',(T)1);
 return NRSMat<T>(result);
 }
 
 
+template <class T>
+const NRMat<T> twoside_transform(const NRMat<T> &S, const NRMat<T> &C, bool transp=0) //calculate C^dagger S C for non-symmetric S
+{
+char tchar='t';
+if(LA_traits<T>::is_complex()) tchar='c';
+if(transp)
+	{
+	NRMat<T> tmp =  C * S;
+	NRMat<T> result(C.nrows(),C.nrows());
+	result.gemm((T)0,tmp,'n',C,tchar,(T)1);
+	return result;
+	}
+NRMat<T> tmp = S * C;
+NRMat<T> result(C.ncols(),C.ncols());
+result.gemm((T)0,C,tchar,tmp,'n',(T)1);
+return result;
+}
+
+
 
 
 template <class T>
@@ -162,6 +183,9 @@ extern void gdiagonalize(NRMat<std::complex<double> > &a, NRVec<double> &wr, NRV
                 NRMat<std::complex<double> > *vl=NULL, NRMat<std::complex<double> > *vr=NULL, const bool corder=1, int n=0, const int sorttype=0, const int biorthonormalize=0,
                 NRMat<std::complex<double> > *b=NULL, NRVec<std::complex<double> > *beta=NULL);
 
+//diagonalization of symmetric real tridiagonal matrix
+extern void diagonalize3(NRVec<double> &d, NRVec<double> &d1, NRMat<double> *v, const bool corder=1, int n0=0);
+
 //complex,real,imaginary parts of various entities
 template<typename T>
 extern const typename LA_traits<T>::realtype realpart(const T&);

smat.cc

@@ -41,12 +41,12 @@ namespace LA {
  * @see NRMat<T>::get(), NRSMat<T>::copyonwrite()
  ******************************************************************************/
 template <typename T>
-void NRSMat<T>::put(int fd, bool dim, bool transp) const {
+void NRSMat<T>::put(int fd, bool dim, bool transp, bool orcaformat) const {
 #ifdef CUDALA
 	if(location != cpu){
 		NRSMat<T> tmp= *this;
 		tmp.moveto(cpu);
-		tmp.put(fd,dim,transp);
+		tmp.put(fd,dim,transp,orcaformat);
 		return;
 	}
 #endif
@@ -54,6 +54,11 @@ void NRSMat<T>::put(int fd, bool dim, bool transp) const {
 	if(dim){
 		if(sizeof(int) != write(fd,&nn,sizeof(int))) laerror("cannot write");
 		if(sizeof(int) != write(fd,&nn,sizeof(int))) laerror("cannot write");
+		if(orcaformat)
+			{
+			int tmp=sizeof(T);
+			if(sizeof(int) != write(fd,&tmp,sizeof(int))) laerror("cannot write");
+			}
 	}
 	LA_traits<T>::multiput((size_t)nn*(nn+1)/2,fd,v,dim);
 }
@@ -66,12 +71,12 @@ void NRSMat<T>::put(int fd, bool dim, bool transp) const {
  * @see NRSMat<T>::put(), NRSMat<T>::copyonwrite()
  ******************************************************************************/
 template <typename T>
-void NRSMat<T>::get(int fd, bool dim, bool transp) {
+void NRSMat<T>::get(int fd, bool dim, bool transp, bool orcaformat) {
 #ifdef CUDALA
 	if(location != cpu){
 		NRSMat<T> tmp;
 		tmp.moveto(cpu);
-		tmp.get(fd,dim,transp);
+		tmp.get(fd,dim,transp,orcaformat);
 		tmp.moveto(location);
 		*this = tmp;
 		return;
@@ -82,6 +87,12 @@ void NRSMat<T>::get(int fd, bool dim, bool transp) {
 	errno = 0;
 	if(dim){
 		if(2*sizeof(int) != read(fd,&nn0,2*sizeof(int))) laerror("cannot read");
+		if(orcaformat)
+			{
+			int tmp;
+			if(sizeof(int) != read(fd,&tmp,sizeof(int))) laerror("cannot read");
+			if(tmp!=sizeof(T)) laerror("mismatch in orca format");
+			}
 		resize(nn0[0]);
 	}else{
 		copyonwrite();

smat.h

@@ -181,10 +181,23 @@ public:
 		return s;
 		}
 
+	const NRVec<T> rsum() const
+		{
+		NRVec<T> r(nn);
+		for(int i=0; i<nn;++i)
+			{
+			r[i]=(T)0;
+			for(int j=0; j<nn; ++j) r[i] += (*this)(i,j);
+			}
+		return r;
+		}
+
+	const NRVec<T> csum() const {return rsum();};
+
 
 	const T trace() const;
-	void get(int fd, bool dimensions = 1, bool transp = 0);
+	void get(int fd, bool dimensions = 1, bool transp = 0, bool orcaformat=false);
-        void put(int fd, bool dimensions = 1, bool transp = 0) const;
+        void put(int fd, bool dimensions = 1, bool transp = 0, bool orcaformat=false) const;
 
 	void copyonwrite(bool detachonly=false, bool deep=true);
 

t.cc

@@ -1137,7 +1137,7 @@ if(0)
 {
 int n,m;
 cin>>n >>m;
-NRSMat<double> a(n,n);
+NRSMat<double> a(n);
 NRVec<double> rr(n);
 
 for(int i=0;i<n;++i) for(int j=0;j<=i;++j)
@@ -3733,6 +3733,7 @@ INDEXGROUP shape;
         {
         shape.number=r;
         shape.symmetry= 1;
+        //shape.symmetry= -1;
         shape.range=n;
         shape.offset=0;
         }
@@ -4392,7 +4393,7 @@ cout <<"Error = "<<(z-zzz).norm()<<endl;
 
 }
 
-if(1)
+if(0)
 {
 //for profiling and timing
 int nn;
@@ -4423,5 +4424,202 @@ cout <<z.norm()<<endl;
 
 }
 
+if(0)
+{
+//tucker test order
+int r,n;
+bool inv;
+cin>>r>>n>>inv;
+NRVec<INDEXGROUP> shape(r);
+for(int i=0; i<r; ++i)
+	{
+	shape[i].number=1;
+	shape[i].symmetry=0;
+	shape[i].range=n+i;
+	shape[i].offset=0;
+	}
+Tensor<double> x(shape);
+x.randomize(1.);
+x.defaultnames();
+cout<<x;
+Tensor<double> x0(x);
+x0.copyonwrite();
+NRVec<NRMat<double> > dec=x.Tucker(1e-12,inv);
+cout<<"Tucker\n"<<x<<endl;
+cout<<dec;
+
+Tensor<double> y = x.inverseTucker(dec,inv);
+cout <<"invTucker\n"<<y;
+cout <<"Error = "<<(x0-y).norm()<<endl;
+
+if(inv==false)
+	{
+	Tensor<double> z = x.linear_transform(dec);
+	cout <<"Error2 = "<<(x0-z).norm()<<endl;
+	}
+}
+
+
+if(0)
+{
+//test linear transform
+int r=3;
+int n,sym;
+cin>>n>>sym;
+NRVec<INDEXGROUP> shape(2);
+        {
+        shape[0].number=r;
+        shape[0].symmetry=sym;
+        shape[0].range=n;
+        shape[0].offset=0;
+        shape[1].number=1;
+        shape[1].symmetry=0;
+        shape[1].range=n;
+        shape[1].offset=0;
+
+        }
+Tensor<double> x(shape);
+x.randomize(1.);
+NRVec<NRMat<double> > t(2);
+t[0].resize(n+2,n); t[0].randomize(1.);
+t[1].resize(n+2,n); t[1].randomize(1.);
+
+Tensor<double> xt=x.linear_transform(t);
+shape.copyonwrite();
+shape[0].range=n+2;
+shape[1].range=n+2;
+Tensor<double> y(shape);
+//this is the most stupid way to compute the transform ;-)
+for(int i=0; i<n+2; ++i) for(int j=0; j<n+2; ++j) for(int k=0; k<n+2; ++k) for(int l=0; l<n+2; ++l)
+	{
+	y.lhs(i,j,k,l)=0;
+	for(int ii=0; ii<n; ++ii) for(int jj=0; jj<n; ++jj) for(int kk=0; kk<n; ++kk) for(int ll=0; ll<n; ++ll)
+		y.lhs(i,j,k,l) += x(ii,jj,kk,ll) * t[0](i,ii) * t[0](j,jj) * t[0](k,kk) * t[1](l,ll) ;
+	}
+cout <<"Error = "<<(xt-y).norm()<<endl;
+
+}
+
+if(0)
+{
+int n,m;
+bool which;
+cin>>n >>m >>which;
+NRSMat<double> a(n);
+NRVec<double> rr(n);
+
+for(int i=0;i<n;++i) for(int j=0;j<=i;++j)
+        {
+        a(i,j)= RANDDOUBLE();
+	if(i==j) a(i,i)+= .5*(i-n*.5);
+        }
+
+NRSMat<double> aa;
+NRMat<double> vv(n,n);
+aa=a; diagonalize(aa,rr,&vv);
+NRVec<double> r(m);
+NRVec<double> *eivecs = new NRVec<double>[m];
+cout <<"Exact energies " <<rr<<endl;
+
+if(which) lanczos(a,r,eivecs,NULL,m,1,1e-6,1,200,300);
+else davidson(a,r,eivecs,NULL,m,1,1e-6,1,200,300);
+cout <<"Iterative energies " <<r;
+cout <<"Eigenvectors compare:\n";
+for(int i=0; i<m; ++i)
+        {
+        cout <<eivecs[i];
+        for(int j=0; j<n;++j) cout <<vv[j][i]<<" ";
+        cout <<endl;
+        }
+
+}
+
+if(0)
+{
+int n,m;
+bool which;
+cin>>n >>m>>which ;
+NRSMat<complex<double> > a(n);
+a.randomize(.1);
+
+for(int i=0;i<n;++i)
+        {
+	a(i,i)=  RANDDOUBLE() +  .5*(i-n);
+        }
+//cout <<"test matrix = "<<a;
+
+NRVec<double> rr(n);
+NRSMat<complex<double> > aa;
+NRMat<complex<double> > vv(n,n);
+aa=a; diagonalize(aa,rr,&vv);
+cout <<"Exact energies "<<rr;
+
+NRVec<complex<double> > r(m);
+NRVec<complex<double> > *eivecs = new NRVec<complex<double> >[m];
+if(which) lanczos(a,r,eivecs,NULL,m,true,1e-5,true,10*n,n);
+else davidson(a,r,eivecs,NULL,m,true,1e-5,true,10*n,n);
+
+cout <<"Davidson/Lanczos energies " <<r;
+cout <<"Exact energies "<<rr;
+
+cout <<"Eigenvectors compare:\n";
+for(int i=0; i<m; ++i) 
+	{
+	cout <<eivecs[i];
+	for(int j=0; j<n;++j) cout <<vv[j][i]<<" ";
+	cout <<endl;
+	}
+
+}
+
+if(1)
+{
+int r,n,sym;
+cin>>r>>n>>sym;
+NRVec<INDEXGROUP> shape(3);
+	shape[0].number=2; 
+        shape[0].symmetry=0;
+        shape[0].range=n+1;
+        shape[0].offset=0;
+
+        shape[1].number=r;
+        shape[1].symmetry= sym;
+        shape[1].range=n;
+        shape[1].offset=0;
+
+        shape[2].number=2;
+        shape[2].symmetry=0;
+        shape[2].range=n+2;
+        shape[2].offset=0;
+
+
+Tensor<double> x(shape); x.randomize(1.);
+x.defaultnames();
+cout <<"x= "<<x.shape << " "<<x.names<<endl;
+
+NRVec<INDEXGROUP> yshape(2);
+        yshape[0].number=1; 
+        yshape[0].symmetry=0;
+        yshape[0].range=n;
+        yshape[0].offset=0;
+        
+        yshape[1].number=1; 
+        yshape[1].symmetry= 0;
+        yshape[1].range=n+3;
+        yshape[1].offset=0;
+
+Tensor<double> y(yshape); y.randomize(1.);
+
+INDEX posit(1,0);
+//Tensor<double> z=x.unwind_index(1,1);
+//Tensor<double> z=x.contraction(INDEX(1,1),y,INDEX(0,0),1,false,false);
+Tensor<double> z=x.linear_transform(1,y);
+
+cout <<z.shape;
+
+//check
+
+
+}
 
 }//main

tensor.cc

@@ -713,6 +713,9 @@ return q;
 template<typename T>
 Tensor<T> Tensor<T>::permute_index_groups(const NRPerm<int> &p) const
 {
+if(p.size()!=shape.size()) laerror("permutation size mismatch in permute_index_groups");
+if(p.is_identity()) return *this;
+
 //std::cout <<"permute_index_groups permutation = "<<p<<std::endl;
 NRVec<INDEXGROUP> newshape=shape.permuted(p,true);
 //std::cout <<"permute_index_groups newshape = "<<newshape<<std::endl;
@@ -836,6 +839,7 @@ if(group==0 && index==0 && shape[0].symmetry==0) //formal split of 1 index witho
 	}
 if(shape[group].number==1) return unwind_index_group(group); //single index in the group
 
+
 //general case - recalculate the shape and allocate the new tensor
 NRVec<INDEXGROUP> newshape(shape.size()+1);
 newshape[0].number=1;
@@ -856,7 +860,10 @@ for(int i=0; i<shape.size(); ++i)
 		--newshape[i+1].number;
 		flatindex += index;
 		}
-	else flatindex += shape[i].number;
+	else 
+		{
+		if(i<group) flatindex += shape[i].number;
+		}
 	}
 
 
@@ -1722,15 +1729,17 @@ if(r==1) //create an analogous output for the trivial case
 	}
 
 //loop over all indices; relies on the fact that unwinding does not change order of remaining indices
+//for inverseorder=false, to avoid inverting order by permute_index_groups we repeatedly unwind the LAST index, and all indices rotate at this position with the first one in the last iteration due to the unwind!
+NRVec<INDEXNAME> names_saved = names;
 for(int i=0; i<r; ++i)
 	{
-	INDEX I=indexposition(i,shape);
+	INDEX I= inverseorder? indexposition(i,shape) : indexposition(r-1,shape);
 	NRMat<T> um;
 	NRVec<INDEXGROUP> ushape;
 	{
 	Tensor<T> uu=unwind_index(I);
-	ushape=uu.shape; //ushape.copyonwrite(); should not be needed
+	ushape=uu.shape; 
-	um=uu.matrix();
+	um=uu.matrix(1);
 	}
 	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());
@@ -1755,28 +1764,30 @@ for(int i=0; i<r; ++i)
 		umnew=u.submatrix(0,umnr-1,0,preserve-1);
 		}
 	else umnew=u;
-	ret[(inverseorder? r-i-1 : i)]=vt.transpose(true);
+	ret[r-i-1]=vt.transpose(true);
 	umnew.diagmultr(w);
 	//rebuild tensor of the preserved shape from matrix
+	ushape.copyonwrite();
 	ushape[0].range=preserve;
 	{
 	NRVec<T> newdata(umnew);
 	umnew.resize(0,0);//deallocate
 	*this = Tensor(ushape,newdata);
+	names=names_saved;
 	}
 	}
 if(!is_flat()) laerror("this should not happen");
-if(!inverseorder)
+if(inverseorder)
 	{
-	NRPerm<int> p(r);
+	NRPerm<int> p(rank()); p.identity();
-	for(int i=1; i<=r; ++i) p[r-i+1]=i;
+	names=names_saved.permuted(p.reverse());
-	*this =  permute_index_groups(p);
 	}
+else 
+	names=names_saved;
 return ret;
 }
 
 
-
 template<typename T>
 Tensor<T> Tensor<T>::inverseTucker(const NRVec<NRMat<T> > &x, bool inverseorder) const
 {
@@ -1784,9 +1795,11 @@ if(rank()!=x.size()) laerror("input of inverseTucker does not match rank");
 Tensor<T> tmp(*this);
 Tensor<T> r;
 if(!is_flat()) laerror("inverseTucker only for flat tensors as produced by Tucker");
+if(inverseorder)
+{
 for(int i=0; i<rank(); ++i)
 	{
-	Tensor<T> mat(x[i],true);
+	Tensor<T> mat(x[i],true); //flat tensor from a matrix
 	r= tmp.contraction(i,0,mat,0,0,(T)1,false,false);
 	if(i<rank()-1) 
 		{
@@ -1794,17 +1807,134 @@ for(int i=0; i<rank(); ++i)
 		r.deallocate();
 		}
 	}
-if(!inverseorder)
+}
+else //not inverseroder
+{
+for(int i=rank()-1; i>=0; --i)
         {
-        NRPerm<int> p(r.rank());
+        Tensor<T> mat(x[i],true); //flat tensor from a matrix
-        for(int i=1; i<=r.rank(); ++i) p[r.rank()-i+1]=i;
+        r= tmp.contraction(rank()-1,0,mat,0,0,(T)1,false,false); //the current index will be the last after previous contractions
-        return r.permute_index_groups(p);
+        if(i>0)
+                {
+                tmp=r;
+                r.deallocate();
+                }
+        }
+}
+if(inverseorder)
+        {
+        NRPerm<int> p(rank()); p.identity();
+        r.names=names.permuted(p.reverse());
         }
 else
-	return r;
+        r.names=names;
+return r;
 }
 
 
+template<typename T>
+Tensor<T> Tensor<T>::linear_transform(const NRVec<NRMat<T> > &x) const
+{
+if(x.size()!=shape.size()) laerror("wrong number of transformation matrices in linear_transform");
+for(int i=0; i<shape.size(); ++i) if(x[i].ncols()!=shape[i].range) laerror("mismatch of transformation matrix size in linear_transform");
+
+Tensor<T> tmp(*this);
+Tensor<T> r;
+
+//do the groups in reverse order to preserve index ordering
+for(int g=shape.size()-1; g>=0; --g)
+	{
+	Tensor<T> mat(x[g],true); //flat tensor from a matrix
+	for(int i=shape[g].number-1; i>=0; --i) //indices in the group in reverse order
+		{
+#ifdef LA_TENSOR_INDEXPOSITION
+		//what should we do with index position?
+		//either set upperindex of mat appropriately, or request ignoring that in contractions?
+#endif
+		r= tmp.contraction(indexposition(rank()-1,tmp.shape),mat,INDEX(0,0),(T)1,false,false); //always the last index
+		if(i>0)
+                	{
+                	tmp=r;
+                	r.deallocate();
+			}
+		}
+	//the group's indices are now individual ones leftmost in tensor r, restore group size and symmetry
+	if(shape[g].number>1)
+		{
+		INDEXLIST il(shape[g].number);
+		for(int i=0; i<shape[g].number; ++i)
+			{
+			il[i].group=i;
+			il[i].index=0;
+			}
+		r = r.merge_indices(il,shape[g].symmetry);
+		}
+	if(g>0) 
+		{
+		tmp=r;
+                r.deallocate();
+		}
+	}
+
+r.names=names;
+return r;
+}
+
+
+template<typename T>
+Tensor<T> Tensor<T>::linear_transform(const int g, const Tensor<T> &x) const
+{
+if(g<0||g>=shape.size()) laerror("wrong index group number in linear_transform");
+if(x.rank()!=2 || x.shape.size()!=2 || x.shape[0].number!=1||x.shape[1].number!=1) laerror("wrong tensor shape for linear_transform");
+if(x.shape[0].range!=shape[g].range) laerror("index range mismatch in linear_transform");
+
+Tensor<T> tmp(*this);
+Tensor<T> r;
+
+
+//contract all indices in the reverse order
+	int gnow=g;
+	for(int i=shape[g].number-1; i>=0; --i) //indices in the group in reverse order
+		{
+		r= tmp.contraction(INDEX(gnow,i),x,INDEX(0,0),(T)1,false,false);
+		++gnow; //new group number in r, one index was added left
+		if(i>0)
+                	{
+                	tmp=r;
+                	r.deallocate();
+			}
+		}
+//the group's indices are now individual ones leftmost in tensor r, restore group size and symmetry
+	if(shape[g].number>1)
+		{
+		INDEXLIST il(shape[g].number);
+		for(int i=0; i<shape[g].number; ++i)
+			{
+			il[i].group=i;
+			il[i].index=0;
+			}
+		r = r.merge_indices(il,shape[g].symmetry);
+		}
+
+//permute the group (now 0) to its original position
+if(g!=0)
+	{
+	NRPerm<int> p(r.shape.size());
+	for(int i=0; i<g; ++i) p[i+1] = (i+1)+1;
+	p[g+1]=1;
+	for(int i=g+1; i<r.shape.size();++i) p[i+1] = i+1;
+	//std::cout<<"perm "<<p;
+	r=r.permute_index_groups(p);
+	}
+
+//preserve names
+r.names=names;
+
+return r;
+}
+
+
+
 template<typename T> 
 int Tensor<T>::findflatindex(const INDEXNAME nam) const
 {
@@ -2116,7 +2246,7 @@ NRVec<NRVec_from1<int> > antigroups;
 parse_antisymmetrizer(antisymmetrizer,antigroups,antinames);
 
 //check the names make sense and fill in the possibly missing ones as separate group
-if(antinames.size()>tmpnames.size()) laerror("too many indices in the antisymmetrizet");
+if(antinames.size()>tmpnames.size()) laerror("too many indices in the antisymmetrizer");
 bitvector isexplicit(tmpnames.size());
 isexplicit.clear();
 for(int i=0; i<antinames.size(); ++i)

tensor.h

@@ -83,6 +83,21 @@ if(LA_traits<T>::is_complex()) //condition known at compile time
 
 }
 
+
+static bool didwarn=false;
+static inline bool ptr_ok(void *ptr)
+{
+#ifdef DEBUG
+if(ptr==NULL && !didwarn) 
+	{
+	std::cout<<"Warning: write to zero element of antisym tensor ignored\n";
+	std::cerr<<"Warning: write to zero element of antisym tensor ignored\n";
+	didwarn=true;
+	}
+#endif
+return (bool)ptr;
+}
+
 template<typename T>
 class Signedpointer
 {
@@ -91,11 +106,11 @@ int sgn;
 public:
 	Signedpointer(T *p, int s) : ptr(p),sgn(s) {};
 	//dereferencing *ptr  should be ignored for sgn==0
-	const T operator=(const T rhs) {*ptr = signeddata(sgn,rhs); return rhs;}
+	const T operator=(const T rhs) {if(ptr_ok(ptr)) *ptr = signeddata(sgn,rhs); return rhs;}
-	void operator*=(const T rhs) {*ptr *= rhs;}
+	void operator*=(const T rhs) {if(ptr_ok(ptr)) *ptr *= rhs;}
-	void operator/=(const T rhs) {*ptr /= rhs;}
+	void operator/=(const T rhs) {if(ptr_ok(ptr)) *ptr /= rhs;}
-	void operator+=(T rhs) {*ptr += signeddata(sgn,rhs);}
+	void operator+=(T rhs) {if(ptr_ok(ptr)) *ptr += signeddata(sgn,rhs);}
-	void operator-=(T rhs) {*ptr -= signeddata(sgn,rhs);}
+	void operator-=(T rhs) {if(ptr_ok(ptr)) *ptr -= signeddata(sgn,rhs);}
 };
 
 
@@ -182,6 +197,8 @@ struct  INDEX
 int group;
 int index;
 bool operator==(const INDEX &rhs) const {return group==rhs.group && index==rhs.index;};
+	INDEX() {};
+	INDEX(int g, int i): group(g), index(i) {};
 };
 typedef NRVec<INDEX> INDEXLIST; //collection of several indices
 
@@ -223,13 +240,16 @@ public:
 	int myrank;
 	NRVec<LA_largeindex> groupsizes; //group sizes of symmetry index groups (a function of shape but precomputed for efficiency)
 	NRVec<LA_largeindex> cumsizes; //cumulative sizes of symmetry index groups (a function of shape but precomputed for efficiency); always cumsizes[0]=1, index group 0 is the innermost-loop one
-
-public:
 	NRVec<INDEXNAME> names;
+
+	//indexing
 	LA_largeindex index(int *sign, const SUPERINDEX &I) const; //map the tensor indices to the position in data
 	LA_largeindex index(int *sign, const  FLATINDEX &I) const; //map the tensor indices to the position in data
 	LA_largeindex vindex(int *sign, LA_index i1, va_list args) const; //map list of indices  to the position in data
 	SUPERINDEX inverse_index(LA_largeindex s) const; //inefficient, but possible if needed
+	int myflatposition(int group, int index) const {return flatposition(group,index,shape);};
+	int myflatposition(const INDEX &i) const {return flatposition(i,shape);};
+	INDEX myindexposition(int flatindex) const {return indexposition(flatindex,shape);};	
 
 	//constructors
 	Tensor() : myrank(-1) {};
@@ -248,7 +268,12 @@ public:
 	explicit Tensor(const NRVec<T> &x);
 	explicit Tensor(const NRMat<T> &x, bool flat=false);
 	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)
+	NRMat<T> matrix(int n=1) const //reinterpret as matrix with column index being the tensor's n leftmost index groups (typically the unwound single index)
+		{
+		if(n<0||n>shape.size()) laerror("wrong number n in tensor:: matrix()");
+		LA_largeindex ncol = (n==shape.size()) ? data.size() : cumsizes[n];
+		return NRMat<T>(data,data.size()/ncol,ncol,0);
+		};
 
 	bool is_named() const {if(names.size()==0) return false; if(names.size()!=myrank) laerror("bad number of index names"); return true;};
 	bool is_uniquely_named() const; //no repeated names
@@ -268,22 +293,16 @@ public:
 	void deallocate() {data.resize(0); shape.resize(0); groupsizes.resize(0); cumsizes.resize(0); names.resize(0);};
 
         inline Signedpointer<T> lhs(const SUPERINDEX &I) {int sign; LA_largeindex i=index(&sign,I);
-#ifdef DEBUG
+				if(sign==0)  return Signedpointer<T>(NULL,sign);
-				if(sign==0) laerror("l-value pointer to nonexistent tensor element"); 
+				else return Signedpointer<T>(&data[i],sign);};
-#endif
-				 return Signedpointer<T>(&data[i],sign);};
 	inline T operator()(const SUPERINDEX &I) const {int sign; LA_largeindex i=index(&sign,I); if(sign==0) return 0; else return signeddata(sign,data[i]);};
         inline Signedpointer<T> lhs(const FLATINDEX &I) {int sign; LA_largeindex i=index(&sign,I); 
-#ifdef DEBUG
+                                if(sign==0) return Signedpointer<T>(NULL,sign);
-                                if(sign==0) laerror("l-value pointer to nonexistent tensor element");
+				else return Signedpointer<T>(&data[i],sign);};
-#endif
-				return Signedpointer<T>(&data[i],sign);};
         inline T operator()(const FLATINDEX &I) const {int sign; LA_largeindex i=index(&sign,I); if(sign==0) return 0; else return signeddata(sign,data[i]);};
         inline Signedpointer<T> lhs(LA_index i1...) {va_list args; int sign; LA_largeindex i; va_start(args,i1); i= vindex(&sign, i1,args);  
-#ifdef DEBUG
+                                if(sign==0) return Signedpointer<T>(NULL,sign);
-                                if(sign==0) laerror("l-value pointer to nonexistent tensor element");
+				else return Signedpointer<T>(&data[i],sign); };
-#endif
-				return Signedpointer<T>(&data[i],sign); };
         inline T operator()(LA_index i1...) const {va_list args; ; int sign; LA_largeindex i; va_start(args,i1);  i= vindex(&sign, i1,args);  if(sign==0) return 0; else return signeddata(sign,data[i]);};
 	
 	inline Tensor& operator=(const Tensor &rhs) {myrank=rhs.myrank; shape=rhs.shape; groupsizes=rhs.groupsizes; cumsizes=rhs.cumsizes; data=rhs.data; names=rhs.names; return *this;};
@@ -399,8 +418,11 @@ public:
 	Tensor merge_indices(const INDEXLIST &il, int symmetry=0) const;	//opposite to flatten (merging with optional symmetrization/antisymmetrization and compression)
 	Tensor merge_indices(const NRVec<INDEXNAME> &nl, int symmetry=0) const {return merge_indices(findindexlist(nl),symmetry);};
 
-	NRVec<NRMat<T> > Tucker(typename LA_traits<T>::normtype thr=1e-12, bool inverseorder=true); //HOSVD-Tucker decomposition, return core tensor in *this, flattened 
+	NRVec<NRMat<T> > Tucker(typename LA_traits<T>::normtype thr=1e-12, bool inverseorder=false); //HOSVD-Tucker decomposition, return core tensor in *this, flattened 
-	Tensor inverseTucker(const NRVec<NRMat<T> > &x, bool inverseorder=true) const; //rebuild the original tensor from Tucker
+	Tensor inverseTucker(const NRVec<NRMat<T> > &x, bool inverseorder=false) const; //rebuild the original tensor from Tucker
+	Tensor linear_transform(const NRVec<NRMat<T> > &x) const; //linear transform by a different matrix per each index group, preserving group symmetries
+	Tensor linear_transform(const int g, const Tensor<T> &x) const; //linear transform a single group, preserve its position and symmetry, x must be rank-2 flat tensor (this is useful for raising/lowering indices)
+	Tensor linear_transform(const int g, const NRMat<T> &x) const {Tensor<T> mat(x,true); return linear_transform(g,mat);}; //linear transform a single group, preserve its position and symmetry
 };
 
 

vec.h

@@ -311,10 +311,10 @@ public:
 
 	//! compute the Euclidean inner product (with conjugation in complex case) 
 	inline const T operator*(const NRVec &rhs) const;
-	inline const T dot(const NRVec &rhs) const {return *this * rhs;};
 
 	//! compute the Euclidean inner product (with conjugation in complex case) with a stride-vector
-	inline const T dot(const T *a, const int stride = 1) const;
+	inline const T dot(const T *a, const int stride = 1, bool conjugate=true) const;
+	inline const T dot(const NRVec &rhs, bool conjugate=true) const {return dot(&(rhs[0]),1,conjugate);};
 
 	void gemv(const T beta, const NRMat<T> &a, const char trans, const T alpha, const NRVec &x);
 	void gemv(const T beta, const NRSMat<T> &a, const char trans /**< just for compatibility reasons */, const T alpha, const NRVec &x);
@@ -455,12 +455,12 @@ public:
 	//! routine for formatted output 
 	void fprintf(FILE *f, const char *format, const int modulo) const;
 	//! routine for unformatted output 
-        void put(int fd, bool dimensions=1, bool transp=0) const;
+        void put(int fd, bool dimensions=1, bool transp=0, bool orca_format=false) const;
 	
 	//! routine for formatted input 
 	void fscanf(FILE *f, const char *format);
 	//! routine for unformatted input 
-        void get(int fd, bool dimensions=1, bool transp=0);
+        void get(int fd, bool dimensions=1, bool transp=0, bool orca_format=false);
 
 	//! constructor creating vector from sparse matrix
 	explicit NRVec(const SparseMat<T> &rhs);
@@ -1073,10 +1073,25 @@ inline const T NRVec<T>::operator*(const NRVec<T> &rhs) const {
 	NOT_GPU(rhs);
 
 	T dot(0);
-	for(register int i=0; i<nn; ++i) dot += v[i]*rhs.v[i];
+	if(LA_traits<T>::is_complex()) for(register int i=0; i<nn; ++i) dot += LA_traits<T>::conjugate(v[i])*rhs.v[i];
+	else for(register int i=0; i<nn; ++i) dot += v[i]*rhs.v[i];
 	return dot;
 }
 
+/***************************************************************************
+ * compute dot product with optional conjugation
+ */
+template<typename T>
+inline const T NRVec<T>::dot(const T *a, const int stride , bool conjugate) const
+{
+	 NOT_GPU(*this);
+	T dot(0);
+        if(LA_traits<T>::is_complex() && conjugate) for(register int i=0; i<nn; ++i) dot += LA_traits<T>::conjugate(v[i])*a[i*stride];
+        else for(register int i=0; i<nn; ++i) dot += v[i]*a[i*stride];
+        return dot; 
+}
+
+
 /***************************************************************************//**
  * indexing operator giving the element at given position with range checking in
  * the DEBUG mode
@@ -1801,9 +1816,9 @@ inline const std::complex<double> NRVec<std::complex<double> >::operator*(const
  * @return \f$\sum_{i=1}^N\vec{x}_{i}\cdot y_{\mathrm{stride}\cdot(i-1) + 1}\f$
  ******************************************************************************/
 template<>
-inline const double NRVec<double>::dot(const double *y, const int stride) const {
+inline const double NRVec<double>::dot(const double *y, const int stride, bool conjugate) const {
 	NOT_GPU(*this);
-	return cblas_ddot(nn, y, stride, v, 1);
+	return cblas_ddot(nn,v, 1,y,stride);
 }
 
 /***************************************************************************//**
@@ -1813,10 +1828,11 @@ inline const double NRVec<double>::dot(const double *y, const int stride) const
  * @return \f$\sum_{i=1}^N\vec{x}_{i}\cdot \overbar{y_{\mathrm{stride}\cdot(i-1) + 1}}\f$
  ******************************************************************************/
 template<>
-inline const std::complex<double> NRVec<std::complex<double> >::dot(const std::complex<double> *y, const int stride) const {
+inline const std::complex<double> NRVec<std::complex<double> >::dot(const std::complex<double> *y, const int stride, bool conjugate) const {
         std::complex<double> dot;
 	NOT_GPU(*this);
-        cblas_zdotc_sub(nn, y, stride, v, 1, &dot);
+        if(conjugate) cblas_zdotc_sub(nn, v,1,y, stride, &dot);
+	else cblas_zdotu_sub(nn, v,1,y, stride, &dot);
         return dot;
 }
 
@@ -2015,12 +2031,12 @@ inline const std::complex<double> NRVec<std::complex<double> >::amin() const {
  * @see NRMat<T>::put()
  ******************************************************************************/
 template <typename T>
-void NRVec<T>::put(int fd, bool dim, bool transp) const {
+void NRVec<T>::put(int fd, bool dim, bool transp, bool orcaformat) const {
 #ifdef CUDALA
 	if(location != cpu){
 		NRVec<T> tmp = *this;
 		tmp.moveto(cpu);
-		tmp.put(fd,dim,transp);
+		tmp.put(fd,dim,transp,orcaformat);
 		return;
 	}
 #endif
@@ -2028,7 +2044,15 @@ void NRVec<T>::put(int fd, bool dim, bool transp) const {
 	int pad(1); //align at least 8-byte
 	if(dim){
 		if(sizeof(int) != write(fd,&nn,sizeof(int))) laerror("write failed");
-		if(sizeof(int) != write(fd,&pad,sizeof(int))) laerror("write failed");
+		if(!orcaformat) 
+			{
+			if(sizeof(int) != write(fd,&pad,sizeof(int))) laerror("write failed");
+			}
+		if(orcaformat)
+			{
+			int tmp=sizeof(T);
+			if(sizeof(int) != write(fd,&tmp,sizeof(int))) laerror("write failed");
+			}
 	}
 	LA_traits<T>::multiput(nn,fd,v,dim);
 }
@@ -2041,12 +2065,12 @@ void NRVec<T>::put(int fd, bool dim, bool transp) const {
  * @see NRMat<T>::get(), copyonwrite()
  ******************************************************************************/
 template <typename T>
-void NRVec<T>::get(int fd, bool dim, bool transp) {
+void NRVec<T>::get(int fd, bool dim, bool transp, bool orcaformat) {
 #ifdef CUDALA
 	if(location != cpu){
 		NRVec<T> tmp;
 		tmp.moveto(cpu);
-		tmp.get(fd,dim,transp);
+		tmp.get(fd,dim,transp,orcaformat);
 		tmp.moveto(location);
 		*this = tmp;
 		return;
@@ -2055,7 +2079,14 @@ void NRVec<T>::get(int fd, bool dim, bool transp) {
 	int nn0[2]; //align at least 8-byte
 	errno = 0;
 	if(dim){
-		if(2*sizeof(int) != read(fd,&nn0,2*sizeof(int))) laerror("read failed");
+		int ndims=orcaformat?1:2;
+		if(ndims*sizeof(int) != read(fd,&nn0,ndims*sizeof(int))) laerror("read failed");
+		if(orcaformat)
+			{
+			int tmp;
+			if(sizeof(int) != read(fd,&tmp,sizeof(int))) laerror("read failed");
+			if(tmp!=sizeof(T)) laerror("mismatch in orca format");
+			}
 		resize(nn0[0]);
 	}else{
 		copyonwrite();