Merge branch 'master' into manuel/doc_update

Author: manuschneider

docs/uv.lock

@@ -41,14 +41,15 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
         return psivec, energy[0].item()
 
 
-    ## Initialiaze MPO
+    ## Initialize MPO
     ##>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
     d = 2
     s = 0.5
     bd_inner = cytnx.Bond(cytnx.BD_KET,[[0],[-2],[2],[0]],[1,1,1,1])
     bd_phys = cytnx.Bond(cytnx.BD_KET,[[1],[-1]],[1,1])
 
-    M = cytnx.UniTensor([bd_inner,bd_inner.redirect(),bd_phys, bd_phys.redirect()]).set_rowrank_(2)
+    M = cytnx.UniTensor([bd_inner,bd_inner.redirect(),bd_phys, bd_phys.redirect()]) \
+                        .set_rowrank_(2).set_name("MPO")
 
     # I
     M.set_elem([0,0,0,0],1)
@@ -68,8 +69,10 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
     q = 0 # conserving glb Qn
     VbdL = cytnx.Bond(cytnx.BD_KET,[[0]],[1])
     VbdR = cytnx.Bond(cytnx.BD_KET,[[q]],[1])
-    L0 = cytnx.UniTensor([bd_inner.redirect(),VbdL.redirect(),VbdL]).set_rowrank_(1) #Left boundary
-    R0 = cytnx.UniTensor([bd_inner,VbdR,VbdR.redirect()]).set_rowrank_(1) #Right boundary
+    L0 = cytnx.UniTensor([bd_inner.redirect(),VbdL.redirect(),VbdL]) \
+                         .set_rowrank_(1).set_name("L0") #Left boundary
+    R0 = cytnx.UniTensor([bd_inner,VbdR,VbdR.redirect()]) \
+                         .set_rowrank_(1).set_name("R0") #Right boundary
     L0.set_elem([0,0,0],1)
     R0.set_elem([3,0,0],1)
 
@@ -81,7 +84,8 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
         cq = -1
     qcntr+=cq
 
-    A[0] = cytnx.UniTensor([VbdL,bd_phys.redirect(),cytnx.Bond(cytnx.BD_BRA,[[qcntr]],[1])]).set_rowrank_(2)
+    A[0] = cytnx.UniTensor([VbdL,bd_phys.redirect(),cytnx.Bond(cytnx.BD_BRA,[[qcntr]],[1])]) \
+        .set_rowrank_(2).set_name("A0")
     A[0].get_block_()[0] = 1
 
     lbls = []
@@ -96,7 +100,7 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
         qcntr+=cq
         B3 = cytnx.Bond(cytnx.BD_BRA,[[qcntr]],[1])
 
-        A[k] = cytnx.UniTensor([B1,B2,B3]).set_rowrank_(2)
+        A[k] = cytnx.UniTensor([B1,B2,B3]).set_rowrank_(2).set_name(f"A{k}")
 
         lbl = [str(2*k),str(2*k+1),str(2*k+2)]
         A[k].relabel_(lbl)
@@ -115,8 +119,11 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
                     "A_Conj: -3,-5,2",\
                     "TOUT: 0;1,2"])
     for p in range(Nsites - 1):
-        anet.PutUniTensors(["L","A","A_Conj","M"],[LR[p],A[p],A[p].Dagger(),M])
+        # Dagger() swaps left/right index order; permute_ restores original label order
+        anet.PutUniTensors(["L","A","A_Conj","M"], \
+                           [LR[p],A[p],A[p].Dagger().permute_(A[p].labels()),M])
         LR[p+1] = anet.Launch()
+        LR[p+1].set_name(f"LR{p+1}")
 
     Ekeep = []
     for k in range(1, numsweeps+1):
@@ -146,8 +153,11 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
                         "M: 0,-2,-4,-5",\
                         "B_Conj: 2,-5,-3",\
                         "TOUT: 0;1,2"])
-            anet.PutUniTensors(["R","B","M","B_Conj"],[LR[p+2],A[p+1],M,A[p+1].Dagger()])
+            # Dagger() swaps index order; permute_ restores it
+            anet.PutUniTensors(["R","B","M","B_Conj"], \
+                               [LR[p+2],A[p+1],M,A[p+1].Dagger().permute_(A[p+1].labels())])
             LR[p+1] = anet.Launch()
+            LR[p+1].set_name(f"LR{p+1}")
 
             print('Sweep[r->l]: %d/%d, Loc: %d,Energy: %f' % (k, numsweeps, p, Ekeep[-1]))
 
@@ -173,21 +183,28 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
             A[p+1] = cytnx.Contract(s,A[p+1]) ## absorb s into next neighbor.
             A[p+1].relabel_(lbls[p+1]) #set the label back to be consistent
 
+            A[p].set_name(f"A{p}")
+            A[p+1].set_name(f"A{p+1}")
+
             # update LR from left to right:
             anet = cytnx.Network()
             anet.FromString(["L: -2,-1,-3",\
                         "A: -1,-4,1",\
                         "M: -2,0,-4,-5",\
                         "A_Conj: -3,-5,2",\
                         "TOUT: 0;1,2"])
-            anet.PutUniTensors(["L","A","A_Conj","M"],[LR[p],A[p],A[p].Dagger(),M])
+            # Dagger() swaps index order; permute_ restores it
+            anet.PutUniTensors(["L","A","A_Conj","M"], \
+                               [LR[p],A[p],A[p].Dagger().permute_(A[p].labels()),M])
             LR[p+1] = anet.Launch()
+            LR[p+1].set_name(f"LR{p+1}")
 
             print('Sweep[l->r]: %d/%d, Loc: %d,Energy: %f' % (k, numsweeps, p, Ekeep[-1]))
 
         A[-1].set_rowrank_(2)
         _,A[-1] = cytnx.linalg.Gesvd(A[-1],is_U=True,is_vT=False) ## last one.
-        A[-1].relabel_(lbls[-1]) #set the label back to be consistent
+        A[-1].set_name(f"A{Nsites-1}")\
+             .relabel_(lbls[-1]) #set the label back to be consistent
 
     return Ekeep
 

example/DMRG/dmrg_two_sites_U1.py

@@ -52,26 +52,29 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
     M[0,0] = M[3,3] = eye
     M[0,1] = M[2,3] = 2**0.5*sp.real()
     M[0,2] = M[1,3] = 2**0.5*sm.real()
-    M = cytnx.UniTensor(M,0)
+    M = cytnx.UniTensor(M,0).set_name("MPO")
 
-    L0 = cytnx.UniTensor.zeros([4,1,1]).set_rowrank_(0) #Left boundary
-    R0 = cytnx.UniTensor.zeros([4,1,1]).set_rowrank_(0) #Right boundary
-    L0[0,0,0] = 1.; R0[3,0,0] = 1.
+    L0 = cytnx.UniTensor.zeros([4,1,1]).set_rowrank_(0).set_name("L0") #Left boundary
+    R0 = cytnx.UniTensor.zeros([4,1,1]).set_rowrank_(0).set_name("R0") #Right boundary
+    L0[0,0,0] = 1.
+    R0[3,0,0] = 1.
 
-    lbls = [] # List for storing the MPS labels
     A = [None for i in range(Nsites)]
-    A[0] = cytnx.UniTensor.normal([1, d, min(chi, d)], 0., 1.).set_rowrank_(2)
-    A[0].relabel_(["0","1","2"])
-    lbls.append(["0","1","2"]) # store the labels for later convinience.
+    A[0] = cytnx.UniTensor.normal([1, d, min(chi, d)], 0., 1.).set_rowrank_(2) \
+                                  .relabel_(["0","1","2"]).set_name("A0")
+
+    lbls = [] # List for storing the MPS labels
+    lbls.append(["0","1","2"]) # store the labels for later convenience.
 
     for k in range(1,Nsites):
         dim1 = A[k-1].shape()[2]; dim2 = d
         dim3 = min(min(chi, A[k-1].shape()[2] * d), d ** (Nsites - k - 1))
-        A[k] = cytnx.UniTensor.normal([dim1, dim2, dim3],0.,1.).set_rowrank_(2)
+        A[k] = cytnx.UniTensor.normal([dim1, dim2, dim3],0.,1.).set_rowrank_(2) \
+                                      .set_name(f"A{k}")
 
         lbl = [str(2*k),str(2*k+1),str(2*k+2)]
         A[k].relabel_(lbl)
-        lbls.append(lbl) # store the labels for later convinience.
+        lbls.append(lbl) # store the labels for later convenience.
 
     LR = [None for i in range(Nsites+1)]
     LR[0]  = L0
@@ -84,23 +87,32 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
         s, A[p] ,vt = cytnx.linalg.Gesvd(A[p])
         A[p+1] = cytnx.Contract(cytnx.Contract(s,vt),A[p+1])
 
-        ## Calculate enviroments:
+        A[p].set_name(f"A{p}")
+        A[p+1].set_name(f"A{p+1}")
+
+        ## Calculate environments:
         anet = cytnx.Network()
         anet.FromString(["L: -2,-1,-3",\
                         "A: -1,-4,1",\
                         "M: -2,0,-4,-5",\
                         "A_Conj: -3,-5,2",\
                         "TOUT: 0,1,2"])
         # or you can do: anet = cytnx.Network("L_AMAH.net")
-        anet.PutUniTensors(["L","A","A_Conj","M"],[LR[p],A[p],A[p].Conj(),M])
+
+        # Dagger() swaps left/right index order; permute_ restores original label order
+        anet.PutUniTensors(["L","A","A_Conj","M"], \
+                           [LR[p],A[p],A[p].Dagger().permute_(A[p].labels()),M])
         LR[p+1] = anet.Launch()
+        LR[p+1].set_name(f"LR{p+1}")
 
         # Recover the original MPS labels
         A[p].relabel_(lbls[p])
         A[p+1].relabel_(lbls[p+1])
 
     _,A[-1] = cytnx.linalg.Gesvd(A[-1],is_U=True,is_vT=False) ## last one.
-    A[-1].relabel_(lbls[-1]) # Recover the original MPS labels
+    A[-1].set_name(f"A{Nsites-1}") \
+         .relabel_(lbls[-1]) # Recover the original MPS labels
+         
 
     Ekeep = []
     for k in range(1, numsweeps+1):
@@ -123,6 +135,9 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
             A[p] = cytnx.Contract(A[p],s) # absorb s into next neighbor
             A[p].relabel_(lbls[p]); # set the label back to be consistent
 
+            A[p].set_name(f"A{p}")
+            A[p+1].set_name(f"A{p+1}")
+
             # update LR from right to left:
             anet = cytnx.Network()
             anet.FromString(["R: -2,-1,-3",\
@@ -131,14 +146,19 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
                             "B_Conj: 2,-5,-3",\
                             "TOUT: 0;1,2"])
             # or you can do: anet = cytnx.Network("R_AMAH.net")
-            anet.PutUniTensors(["R","B","M","B_Conj"],[LR[p+2],A[p+1],M,A[p+1].Conj()])
+            
+            # Dagger() swaps index order; permute_ restores it
+            anet.PutUniTensors(["R","B","M","B_Conj"], \
+                               [LR[p+2],A[p+1],M,A[p+1].Dagger().permute_(A[p+1].labels())])
             LR[p+1] = anet.Launch()
+            LR[p+1].set_name(f"LR{p+1}")
 
             print('Sweep[r->l]: %d/%d, Loc: %d,Energy: %f' % (k, numsweeps, p, Ekeep[-1]))
 
         A[0].set_rowrank_(1)
         _,A[0] = cytnx.linalg.Gesvd(A[0],is_U=False, is_vT=True)
         A[0].relabel_(lbls[0]); #set the label back to be consistent
+        A[0].set_name("A0")
 
         for p in range(Nsites-1):
             dim_l = A[p].shape()[0]
@@ -158,6 +178,9 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
             A[p+1] = cytnx.Contract(s,A[p+1]) ## absorb s into next neighbor.
             A[p+1].relabel_(lbls[p+1]); #set the label back to be consistent
 
+            A[p].set_name(f"A{p}")
+            A[p+1].set_name(f"A{p+1}")
+
             # update LR from left to right:
             anet = cytnx.Network()
             anet.FromString(["L: -2,-1,-3",\
@@ -167,14 +190,18 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
                             "TOUT: 0,1,2"])
             # or you can do: anet = cytnx.Network("L_AMAH.net")
 
-            anet.PutUniTensors(["L","A","A_Conj","M"],[LR[p],A[p],A[p].Conj(),M])
+            # Dagger() swaps index order; permute_ restores it
+            anet.PutUniTensors(["L","A","A_Conj","M"], \
+                               [LR[p],A[p],A[p].Dagger().permute_(A[p].labels()),M])
             LR[p+1] = anet.Launch()
+            LR[p+1].set_name(f"LR{p+1}")
 
             print('Sweep[l->r]: %d/%d, Loc: %d,Energy: %f' % (k, numsweeps, p, Ekeep[-1]))
 
         A[-1].set_rowrank_(2)
         _,A[-1] = cytnx.linalg.Gesvd(A[-1],is_U=True,is_vT=False) ## last one.
-        A[-1].relabel_(lbls[-1]); #set the label back to be consistent
+        A[-1].set_name(f"A{Nsites-1}") \
+             .relabel_(lbls[-1]); #set the label back to be consistent
     return Ekeep
 
 if __name__ == '__main__':

example/DMRG/dmrg_two_sites_dense.py

@@ -81,7 +81,9 @@ def get_energy(A, M):
                         "TOUT: 0,1,2"])
         # or you can do: anet = cytnx.Network("L_AMAH.net")
         for p in range(0, N):
-            anet.PutUniTensors(["L","A","A_Conj","M"],[L,A[p],A[p].Conj(),M])
+            # Dagger() swaps left/right index order; permute_ restores original label order
+            anet.PutUniTensors(["L","A","A_Conj","M"], \
+                               [L,A[p],A[p].Dagger().permute_(A[p].labels()),M])
             L = anet.Launch()
         E = cytnx.Contract(L, R0).item()
         print('energy:', E)
@@ -140,7 +142,8 @@ def get_energy(A, M):
                         "A_Conj: -3,-5,2",\
                         "TOUT: 0,1,2"])
         # or you can do: anet = cytnx.Network("L_AMAH.net")
-        anet.PutUniTensors(["L","A","A_Conj","M"],[LR[p],A[p],A[p].Conj(),M])
+        anet.PutUniTensors(["L","A","A_Conj","M"], \
+                           [LR[p],A[p],A[p].Dagger().permute_(A[p].labels()),M])  # Dagger() swaps index order; permute_ restores it
         LR[p+1] = anet.Launch()
 
         # Recover the original MPS labels
@@ -177,7 +180,8 @@ def get_energy(A, M):
                             "B_Conj: 2,-5,-3",\
                             "TOUT: ;0,1,2"])
             # or you can do: anet = cytnx.Network("R_AMAH.net")
-            anet.PutUniTensors(["R","B","M","B_Conj"],[LR[p+1],A[p],M,A[p].Conj()])
+            anet.PutUniTensors(["R","B","M","B_Conj"], \
+                               [LR[p+1],A[p],M,A[p].Dagger().permute_(A[p].labels())])  # Dagger() swaps index order; permute_ restores it
             old_LR = LR[p].clone()
             if p != 0:
                 LR[p] = anet.Launch()
@@ -217,7 +221,8 @@ def get_energy(A, M):
                             "A_Conj: -3,-5,2",\
                             "TOUT: 0,1,2"])
 
-            anet.PutUniTensors(["L","A","A_Conj","M"],[LR[p],A[p],A[p].Conj(),M])
+            anet.PutUniTensors(["L","A","A_Conj","M"], \
+                               [LR[p],A[p],A[p].Dagger().permute_(A[p].labels()),M])  # Dagger() swaps index order; permute_ restores it
             old_LR = LR[p+1].clone()
 
 
@@ -248,15 +253,17 @@ def Local_meas(A, B, Op, site):
                     "TOUT: 2;4"])
     for i in range(0, N):
         if i != site:
-            anet.PutUniTensors(["l","A","B"],[l,A[i],B[i].Conj()])
+            anet.PutUniTensors(["l","A","B"], \
+                               [l,A[i],B[i].Dagger().permute_(B[i].labels())])  # Dagger() swaps index order; permute_ restores it
             l = anet.Launch()
         else:
             tmp = A[i].relabel(1, "_aux_up")
             Op = Op.relabel(["_aux_up", "_aux_low"])
             tmp = cytnx.Contract(tmp, Op)
             tmp.relabel_("_aux_low", A[i].labels()[1])
             tmp.permute_(A[i].labels())
-            anet.PutUniTensors(["l","A","B"],[l,tmp,B[i].Conj()])
+            anet.PutUniTensors(["l","A","B"], \
+                               [l,tmp,B[i].Dagger().permute_(B[i].labels())])  # Dagger() swaps index order; permute_ restores it
             l = anet.Launch()
 
     return l.reshape(1).item()

example/TDVP/tdvp1_dense.py

@@ -799,7 +799,7 @@ namespace cytnx {
 
     /**
     @brief Group the duplicated quantum number and return the new instance
-            of the Bond ojbect.
+            of the Bond object.
         @details This function will group the duplicated quantum number and return
             the new instance of the Bond object. It will also the \p mapper, where
                 \p mapper is about the new index from old index via\n

include/Bond.hpp

@@ -5000,16 +5000,17 @@ namespace cytnx {
 
     /**
     @brief Take the transpose of the UniTensor.
-    @details This function will take the transpose of the UniTensor. If the UniTensor is
-      tagged (i.e. the Bonds are directional), it will swap the direction of the Bonds but
-      the rowrank will not change. If the UniTensor is untagged (i.e. the Bonds are
-      BondType::BD_REG), it will change the rowrank to the opposite side.
-      For fermionic UniTensors, the index order will be reversed without sign flips, and the
-    direction of all Bonds will swapped.
-        @return UniTensor
+    @details This function takes the transpose of a UniTensor:
+      1)  The order of the indices is inverted.
+      2)  Incoming legs become outgoing ones, and vice versa.
+      3)  The rowrank is set to rank - old rowrank, such that left indices become right indices and
+          vice versa.
+    @return UniTensor
+    @note This function does not only exchange left- and right indices, but inverts the order of all
+    indices.
     @note Compared to Transpose_(), this function will return new UniTensor object.
-        @see Transpose_()
-        */
+    @see Transpose_()
+    */
     UniTensor Transpose() const {
       UniTensor out;
       out._impl = this->_impl->Transpose();
@@ -5018,10 +5019,11 @@ namespace cytnx {
 
     /**
     @brief Take the transpose of the UniTensor, inplacely.
-        @return UniTensor
+    @return UniTensor
+    @note This function inverts the order of all indices.
     @note Compared to Transpose(), this function is an inplace function.
-        @see Transpose()
-        */
+    @see Transpose()
+    */
     UniTensor &Transpose_() {
       this->_impl->Transpose_();
       return *this;
@@ -5126,10 +5128,11 @@ namespace cytnx {
 
     /**
     @brief Take the conjugate transpose to the UniTensor.
-        @return UniTensor
-    @note Compared to Dagger_(), this function will create a new UniTensor ojbect.
-        @see Dagger_(), Transpose()
-        */
+    @return UniTensor
+    @note This function inverts the order of all indices.
+    @note Compared to Dagger_(), this function will create a new UniTensor object.
+    @see Dagger_(), Transpose()
+    */
     UniTensor Dagger() const {
       UniTensor out;
       out._impl = this->_impl->Dagger();
@@ -5138,10 +5141,11 @@ namespace cytnx {
 
     /**
     @brief Take the conjugate transpose to the UniTensor, inplacely.
-        @return UniTensor&
+    @return UniTensor&
+    @note This function inverts the order of all indices.
     @note Compared to Dagger(), this is an inplace function.
-        @see Dagger()
-        */
+    @see Dagger()
+    */
     UniTensor &Dagger_() {
       this->_impl->Dagger_();
       return *this;

include/UniTensor.hpp

@@ -4,7 +4,7 @@ v1.0.0
 3. [Change] Merge relabel and relabels into relabel, and relabel_ and relabels_ into relabel_.
 4. [New] Add an optional argument min_blockdim to svd_truncate to define a minimum dimension for each block.
 5. [New] Add Eig/Eigh functions for Block UniTensor.
-6. [New] Add Lancos-like algoirthm, Lanczos_Exp, to approximate exponential operator acting on a state.
+6. [New] Add Lanczos-like algoirthm, Lanczos_Exp, to approximate exponential operator acting on a state.
 7. [Change] Migrate cuTENSOR APIs to the version 2.
 8. [Change] reshape_ and permute_ to return the object itself instead of None.
 9. [Change] Remove the magma dependency.