From 087fd35a00d506017a9bfddbc8c95e074414bcc4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Patrick=20Gel=C3=9F?=
 <38036185+PGelss@users.noreply.github.com>
Date: Wed, 24 Jul 2024 11:00:57 +0200
Subject: [PATCH] adapted ALS (EVP) to complex cases

---
 scikit_tt/solvers/evp.py | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/scikit_tt/solvers/evp.py b/scikit_tt/solvers/evp.py
index bd9c50a..89f5b7b 100644
--- a/scikit_tt/solvers/evp.py
+++ b/scikit_tt/solvers/evp.py
@@ -278,19 +278,18 @@ def __construct_left_stacks(i: int, trains, stacks):
     else:
 
         # contract previous stack element with solution and operator cores
-        stacks.op_left[i] = np.tensordot(stacks.op_left[i - 1], trains.solution.cores[i - 1][:, :, 0, :], axes=(0, 0))
+        stacks.op_left[i] = np.tensordot(stacks.op_left[i - 1], np.conjugate(trains.solution.cores[i - 1][:, :, 0, :]), axes=(0, 0))
         stacks.op_left[i] = np.tensordot(stacks.op_left[i], trains.operator.cores[i - 1], axes=([0, 2], [0, 2]))
         stacks.op_left[i] = np.tensordot(stacks.op_left[i], trains.solution.cores[i - 1][:, :, 0, :], axes=([0, 2], [0, 1]))
 
         if trains.operator_gevp is not None:
-            stacks.op_gevp_left[i] = np.tensordot(stacks.op_gevp_left[i - 1], trains.solution.cores[i - 1][:, :, 0, :], axes=(0, 0))
+            stacks.op_gevp_left[i] = np.tensordot(stacks.op_gevp_left[i - 1], np.conjugate(trains.solution.cores[i - 1][:, :, 0, :]), axes=(0, 0))
             stacks.op_gevp_left[i] = np.tensordot(stacks.op_gevp_left[i], trains.operator_gevp.cores[i - 1], axes=([0, 2], [0, 2]))
             stacks.op_gevp_left[i] = np.tensordot(stacks.op_gevp_left[i], trains.solution.cores[i - 1][:, :, 0, :], axes=([0, 2], [0, 1]))
 
         for j in range(len(trains.previous)):
             stacks.previous_left[j][i] = np.tensordot(stacks.previous_left[j][i - 1], trains.previous[j].cores[i - 1][:, :, 0, :], axes=(0, 0))
-            stacks.previous_left[j][i] = np.tensordot(stacks.previous_left[j][i], trains.solution.cores[i - 1][:, :, 0, :], axes=([0, 1], [0, 1]))
-
+            stacks.previous_left[j][i] = np.tensordot(stacks.previous_left[j][i], np.conjugate(trains.solution.cores[i - 1][:, :, 0, :]), axes=([0, 1], [0, 1]))
 
 
 def __construct_right_stacks(i: int, trains, stacks):
@@ -321,17 +320,17 @@ def __construct_right_stacks(i: int, trains, stacks):
     else:
 
         # contract previous stack element with solution and operator cores
-        stacks.op_right[i] = np.tensordot(trains.solution.cores[i + 1][:, :, 0, :], stacks.op_right[i + 1], axes=(2, 2))
+        stacks.op_right[i] = np.tensordot(np.conjugate(trains.solution.cores[i + 1][:, :, 0, :]), stacks.op_right[i + 1], axes=(2, 2))
         stacks.op_right[i] = np.tensordot(trains.operator.cores[i + 1], stacks.op_right[i], axes=([1, 3], [1, 3]))
         stacks.op_right[i] = np.tensordot(trains.solution.cores[i + 1][:, :, 0, :], stacks.op_right[i], axes=([1, 2], [1, 3]))
 
         if trains.operator_gevp is not None:
-            stacks.op_gevp_right[i] = np.tensordot(trains.solution.cores[i + 1][:, :, 0, :], stacks.op_gevp_right[i + 1], axes=(2, 2))
+            stacks.op_gevp_right[i] = np.tensordot(np.conjugate(trains.solution.cores[i + 1][:, :, 0, :]), stacks.op_gevp_right[i + 1], axes=(2, 2))
             stacks.op_gevp_right[i] = np.tensordot(trains.operator_gevp.cores[i + 1], stacks.op_gevp_right[i], axes=([1, 3], [1, 3]))
             stacks.op_gevp_right[i] = np.tensordot(trains.solution.cores[i + 1][:, :, 0, :], stacks.op_gevp_right[i], axes=([1, 2], [1, 3]))
 
         for j in range(len(trains.previous)):
-            stacks.previous_right[j][i] = np.tensordot(trains.solution.cores[i + 1][:, :, 0, :], stacks.previous_right[j][i + 1], axes=(2, 1))   
+            stacks.previous_right[j][i] = np.tensordot(np.conjugate(trains.solution.cores[i + 1][:, :, 0, :]), stacks.previous_right[j][i + 1], axes=(2, 1))   
             stacks.previous_right[j][i] = np.tensordot(trains.previous[j].cores[i + 1][:, :, 0, :], stacks.previous_right[j][i], axes=([1, 2], [1, 2]))
 
 
@@ -379,7 +378,7 @@ def __construct_micro_matrices(i: int, trains, stacks, shift: float) -> np.ndarr
         tmp = np.tensordot(tmp, stacks.previous_right[j][i], axes=(2, 0))
         tmp = tmp.reshape(trains.solution.ranks[i] * trains.previous[j].row_dims[i] * trains.solution.ranks[i + 1], 1)
 
-        micro_op += shift*tmp.dot(tmp.T)
+        micro_op += shift*tmp.dot(np.conjugate(tmp.T))
 
     return micro_op, micro_op_gevp
 
@@ -435,13 +434,13 @@ def __update_core(i: int, micro_op: np.ndarray,
     if solver == 'eigh':
         eigenvalues, eigenvectors = lin.eigh(micro_op, b=micro_op_gevp, overwrite_a=True, overwrite_b=True,
                                              check_finite=False,
-                                             subset_by_index=(micro_op.shape[0] - number_ev, micro_op.shape[0] - 1))
+                                             eigvals=(micro_op.shape[0] - number_ev, micro_op.shape[0] - 1))
         eigenvalues  = eigenvalues[::-1]
         eigenvectors = eigenvectors[:, ::-1]
 
     if real is True:
         eigenvalues  = np.real(eigenvalues)
-        eigenvectors = np.real(eigenvectors)
+        #eigenvectors = np.real(eigenvectors)
 
     # reshape solution and orthonormalization
     # ---------------------------------------