diff --git a/README.md b/README.md
index 9aff1cd..82af55b 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,6 @@ This document provides comprehensive guidance on using SymPT, detailing its key
    - [Schrieffer-Wolff Transformation (SWT)](#schrieffer-wolff-transformation-swt)
    - [Full-Diagonalization (FD)](#full-diagonalization-fd)
    - [Arbitrary Coupling Elimination (ACE)](#arbitrary-coupling-elimination-ace)
-   - [Least-Action Multi-Block Transformations](#least-action-multi-block-transformations)
 7. [Advanced Tools](#advanced-tools)
 8. [Contributing](#contributing)
 9. [License](#license)
diff --git a/examples/C1_Quantum_rabi_model.ipynb b/examples/C1_Quantum_rabi_model.ipynb
index c2ca68d..29f71e5 100644
--- a/examples/C1_Quantum_rabi_model.ipynb
+++ b/examples/C1_Quantum_rabi_model.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -221,7 +221,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Converting to operator form: 100%|███████████████| 3/3 [00:00<00:00, 226.93it/s]\n"
+      "Converting to operator form: 100%|██████████| 3/3 [00:00<00:00, 212.57it/s]\n"
      ]
     },
     {
@@ -253,7 +253,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "sympt",
    "language": "python",
    "name": "python3"
   },
@@ -267,7 +267,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.1"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/examples/C2_Quantum_rabi_TD.ipynb b/examples/C2_Quantum_rabi_TD.ipynb
index 1a2d7e8..245c071 100644
--- a/examples/C2_Quantum_rabi_TD.ipynb
+++ b/examples/C2_Quantum_rabi_TD.ipynb
@@ -131,9 +131,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing the effective Hamiltonian: 100%|████████| 2/2 [00:00<00:00, 10.10it/s]\n",
+      "Computing the effective Hamiltonian: 100%|██████████| 2/2 [00:00<00:00, 10.43it/s]\n",
       "\u001b[32mThe Hamiltonian has been solved successfully. Please use the get_H method to get the result in the desired form.\u001b[0m\n",
-      "Converting to operator form: 100%|████████████████| 2/2 [00:00<00:00, 42.23it/s]\n"
+      "Converting to operator form: 100%|██████████| 2/2 [00:00<00:00, 37.09it/s]\n"
      ]
     },
     {
@@ -191,8 +191,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Rotating the expression: 100%|███████████████████| 2/2 [00:00<00:00, 293.80it/s]\n",
-      "Converting to operator form: 100%|███████████████| 3/3 [00:00<00:00, 204.67it/s]\n"
+      "Rotating the expression: 100%|██████████| 2/2 [00:00<00:00, 297.06it/s]\n",
+      "Converting to operator form: 100%|██████████| 3/3 [00:00<00:00, 175.88it/s]\n"
      ]
     },
     {
@@ -249,9 +249,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing the effective Hamiltonian: 100%|████████| 2/2 [00:00<00:00,  9.70it/s]\n",
+      "Computing the effective Hamiltonian: 100%|██████████| 2/2 [00:00<00:00,  8.27it/s]\n",
       "\u001b[32mThe Hamiltonian has been solved successfully. Please use the get_H method to get the result in the desired form.\u001b[0m\n",
-      "Converting to operator form: 100%|████████████████| 2/2 [00:00<00:00,  6.79it/s]\n"
+      "Converting to operator form: 100%|██████████| 2/2 [00:00<00:00,  5.81it/s]\n"
      ]
     }
    ],
@@ -357,7 +357,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "sympt",
    "language": "python",
    "name": "python3"
   },
@@ -371,7 +371,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.1"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/examples/C3_EDSR.ipynb b/examples/C3_EDSR.ipynb
index c5ae66c..48a1d79 100644
--- a/examples/C3_EDSR.ipynb
+++ b/examples/C3_EDSR.ipynb
@@ -135,9 +135,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing the effective Hamiltonian: 100%|████████| 5/5 [00:16<00:00,  3.37s/it]\n",
+      "Computing the effective Hamiltonian: 100%|██████████| 5/5 [00:07<00:00,  1.54s/it]\n",
       "\u001b[32mThe Hamiltonian has been solved successfully. Please use the get_H method to get the result in the desired form.\u001b[0m\n",
-      "Converting to matrix form: 100%|██████████████████| 6/6 [00:00<00:00, 57.79it/s]\n"
+      "Converting to matrix form: 100%|██████████| 6/6 [00:00<00:00, 58.80it/s]\n"
      ]
     }
    ],
@@ -352,7 +352,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "sympt",
    "language": "python",
    "name": "python3"
   },
@@ -366,7 +366,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.1"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/examples/C4_ParametricLightInteraction.ipynb b/examples/C4_ParametricLightInteraction.ipynb
index ed2f9ba..e3c9838 100644
--- a/examples/C4_ParametricLightInteraction.ipynb
+++ b/examples/C4_ParametricLightInteraction.ipynb
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -151,7 +151,7 @@
        "2*|{g_p}|**2*(omega_p**2 + omega_+*omega_-)*(omega_+ - omega_-)*cos(omega_p*t + phi_p)**2/((-omega_p + omega_+)*(-omega_p + omega_-)*(omega_p + omega_+)*(omega_p + omega_-))"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "scrolled": true
    },
@@ -187,7 +187,7 @@
        "2*|{g_p}|**2*(omega_p**2 + omega_+*omega_-)*(omega_+ - omega_-)*cos(omega_p*t + phi_p)**2/((-omega_p + omega_+)*(-omega_p + omega_-)*(omega_p + omega_+)*(omega_p + omega_-))"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -213,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -225,7 +225,7 @@
        "-4*|{g_p}|**2*(-omega_p**2 + omega_+*omega_-)*(omega_+ + omega_-)*cos(omega_p*t + phi_p)**2/((-omega_p + omega_+)*(-omega_p + omega_-)*(omega_p + omega_+)*(omega_p + omega_-))"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -244,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -256,7 +256,7 @@
        "-2*|{g_p}|**2*(-omega_p**2 + omega_+*omega_-)*(omega_+ + omega_-)*(cos(2*omega_p*t + 2*phi_p) + 1)/((-omega_p + omega_+)*(-omega_p + omega_-)*(omega_p + omega_+)*(omega_p + omega_-))"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -282,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -294,7 +294,7 @@
        "-|{g_p}|**2*(omega_+ + omega_-)*(-omega_p**2*cos(2*omega_p*t + 2*phi_p) - omega_p**2 - I*omega_p*omega_+*sin(2*omega_p*t + 2*phi_p) + I*omega_p*omega_-*sin(2*omega_p*t + 2*phi_p) + omega_+*omega_-*cos(2*omega_p*t + 2*phi_p) + omega_+*omega_-)/((-omega_p + omega_+)*(-omega_p + omega_-)*(omega_p + omega_+)*(omega_p + omega_-))"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -313,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -325,7 +325,7 @@
        "-|{g_p}|**2*(omega_+ + omega_-)*(-omega_p**2*cos(2*omega_p*t + 2*phi_p) - omega_p**2 - I*omega_p*omega_+*sin(2*omega_p*t + 2*phi_p) + I*omega_p*omega_-*sin(2*omega_p*t + 2*phi_p) + omega_+*omega_-*cos(2*omega_p*t + 2*phi_p) + omega_+*omega_-)/((-omega_p + omega_+)*(-omega_p + omega_-)*(omega_p + omega_+)*(omega_p + omega_-))"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -345,7 +345,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "sympt",
    "language": "python",
    "name": "python3"
   },
@@ -359,7 +359,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.1"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/examples/C5_EDSR Crosstalk.ipynb b/examples/C5_EDSR Crosstalk.ipynb
index cf0ce23..fc4e1ae 100644
--- a/examples/C5_EDSR Crosstalk.ipynb	
+++ b/examples/C5_EDSR Crosstalk.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -114,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -139,25 +139,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing the effective Hamiltonian: 100%|████████| 5/5 [00:25<00:00,  5.11s/it]\n",
+      "Computing the effective Hamiltonian: 100%|██████████| 5/5 [00:08<00:00,  1.70s/it]\n",
       "\u001b[32mThe Hamiltonian has been solved successfully. Please use the get_H method to get the result in the desired form.\u001b[0m\n",
-      "Converting to matrix form: 100%|██████████████████| 6/6 [00:00<00:00, 43.05it/s]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Computing the effective Hamiltonian: 100%|██████████| 5/5 [00:11<00:00,  2.32s/it]\n",
-      "\u001b[32mThe Hamiltonian has been solved successfully. Please use the get_H method to get the result in the desired form.\u001b[0m\n",
-      "Converting to matrix form: 100%|██████████| 6/6 [00:00<00:00, 121.16it/s]\n"
+      "Converting to matrix form: 100%|██████████| 6/6 [00:00<00:00, 47.09it/s]\n"
      ]
     }
    ],
@@ -170,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -204,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -216,7 +207,7 @@
        "-E_{z}*sigma_3/2"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -229,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -241,7 +232,7 @@
        "-\\tilde{E}_{\\mathrm{ac}}**2*sin(omega*t)**2*sigma_0/(hbar*omega0) - 2*\\tilde{E}_{\\mathrm{ac}}*\\tilde{b}_{\\mathrm{SL}}*sin(\\theta)*sin(omega*t)*sigma_3/(hbar*omega0) - 2*\\tilde{E}_{\\mathrm{ac}}*\\tilde{b}_{\\mathrm{SL}}*sin(omega*t)*cos(\\theta)*sigma_2/(hbar*omega0) - \\tilde{b}_{\\mathrm{SL}}**2*sigma_0/(hbar*omega0)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -254,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -266,7 +257,7 @@
        "-E_{z}*\\tilde{b}_{\\mathrm{SL}}**2*sin(\\theta)*cos(\\theta)*sigma_2/(hbar**2*omega0**2) + E_{z}*\\tilde{b}_{\\mathrm{SL}}**2*cos(\\theta)**2*sigma_3/(hbar**2*omega0**2)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -279,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -291,7 +282,7 @@
        "-E_{z}**2*\\tilde{E}_{\\mathrm{ac}}*\\tilde{b}_{\\mathrm{SL}}*sin(omega*t)*cos(\\theta)*sigma_2/(hbar**3*omega0**3) - E_{z}**2*\\tilde{b}_{\\mathrm{SL}}**2*cos(\\theta)**2*sigma_0/(hbar**3*omega0**3) + \\tilde{E}_{\\mathrm{ac}}**2*omega**2*cos(2*omega*t)*sigma_0/(2*hbar*omega0**3) - \\tilde{E}_{\\mathrm{ac}}**2*omega**2*sigma_0/(2*hbar*omega0**3) - \\tilde{E}_{\\mathrm{ac}}*\\tilde{b}_{\\mathrm{SL}}*omega**2*sin(\\theta)*sin(omega*t)*sigma_3/(hbar*omega0**3) - \\tilde{E}_{\\mathrm{ac}}*\\tilde{b}_{\\mathrm{SL}}*omega**2*sin(omega*t)*cos(\\theta)*sigma_2/(hbar*omega0**3)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -304,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -316,7 +307,7 @@
        "-E_{z}**3*\\tilde{b}_{\\mathrm{SL}}**2*sin(\\theta)*cos(\\theta)*sigma_2/(hbar**4*omega0**4) + E_{z}**3*\\tilde{b}_{\\mathrm{SL}}**2*cos(\\theta)**2*sigma_3/(hbar**4*omega0**4) - 4*E_{z}*\\tilde{E}_{\\mathrm{ac}}**2*\\tilde{b}_{\\mathrm{SL}}**2*sin(\\theta)*sin(omega*t)**2*cos(\\theta)*sigma_2/(hbar**4*omega0**4) + 4*E_{z}*\\tilde{E}_{\\mathrm{ac}}**2*\\tilde{b}_{\\mathrm{SL}}**2*sin(omega*t)**2*cos(\\theta)**2*sigma_3/(hbar**4*omega0**4) + E_{z}*\\tilde{b}_{\\mathrm{SL}}**4*sin(\\theta)*cos(\\theta)*sigma_2/(hbar**4*omega0**4) - E_{z}*\\tilde{b}_{\\mathrm{SL}}**4*cos(\\theta)**2*sigma_3/(hbar**4*omega0**4)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -337,7 +328,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "sympt",
    "language": "python",
    "name": "python3"
   },
@@ -351,7 +342,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.1"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/examples/F1_Stochastic_Matrix_ACE_SymPT.ipynb b/examples/F1_Stochastic_Matrix_ACE_SymPT.ipynb
index 9d8a5f8..a9c5e53 100644
--- a/examples/F1_Stochastic_Matrix_ACE_SymPT.ipynb
+++ b/examples/F1_Stochastic_Matrix_ACE_SymPT.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "aa882a4e-a547-4f60-b250-e76c64a0fe43",
    "metadata": {},
    "outputs": [],
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "f826769d-6603-4ee9-82ed-800fa4c43dc1",
    "metadata": {},
    "outputs": [],
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "0c132d6d-d7d4-4a56-b563-609175698744",
    "metadata": {},
    "outputs": [],
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "bdb99a3d-f723-4e1d-8414-7e6afa4d3b16",
    "metadata": {},
    "outputs": [
@@ -106,7 +106,7 @@
        "(37, 37)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "0bdb5fec-4c4c-4227-8bcc-7dcb7454456d",
    "metadata": {},
    "outputs": [],
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "6a46c876-a2ce-40bd-9ac9-12f31513fe64",
    "metadata": {},
    "outputs": [],
@@ -194,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "b5c5b2ae-0740-456b-921d-4ff40277eba0",
    "metadata": {},
    "outputs": [
@@ -213,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "ce4f6d28-e51f-451d-ad2c-3b0236e954fd",
    "metadata": {},
    "outputs": [
@@ -222,9 +222,9 @@
      "output_type": "stream",
      "text": [
       "\u001b[32mSubstituting the symbol values in the Hamiltonian and perturbative interactions.\u001b[0m\n",
-      "Computing the effective Hamiltonian: 100%|████████| 3/3 [00:36<00:00, 12.09s/it]\n",
+      "Computing the effective Hamiltonian: 100%|██████████| 3/3 [00:21<00:00,  7.29s/it]\n",
       "\u001b[32mThe Hamiltonian has been solved successfully. Please use the get_H method to get the result in the desired form.\u001b[0m\n",
-      "Converting to matrix form: 100%|██████████████████| 4/4 [00:00<00:00, 96.78it/s]\n"
+      "Converting to matrix form: 100%|██████████| 4/4 [00:00<00:00, 83.49it/s]\n"
      ]
     }
    ],
@@ -245,23 +245,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "0a948bbf",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x72572d423b60>"
+       "<matplotlib.colorbar.Colorbar at 0x776acc1a2d50>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -281,23 +281,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "c9c2200c-2e37-47c8-aaf0-4b044a748c9c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x725723665310>"
+       "<matplotlib.colorbar.Colorbar at 0x776aae3e8050>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -327,7 +327,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "sympt",
    "language": "python",
    "name": "python3"
   },
@@ -341,7 +341,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.1"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/sympt/__init__.py b/sympt/__init__.py
index f7a89cb..101482a 100644
--- a/sympt/__init__.py
+++ b/sympt/__init__.py
@@ -3,4 +3,4 @@
 from .utils import *
 from .solver import *
 
-__version__ = "1.1.0"
+__version__ = "1.1.1"
diff --git a/sympt/solver.py b/sympt/solver.py
index 6f4a124..8350804 100644
--- a/sympt/solver.py
+++ b/sympt/solver.py
@@ -2,7 +2,7 @@
 """
 Branch: Optimizations branch
 Title: Solver for sympt package
-Date: 24 December 2024
+Date: 8 January 2025
 Authors:
 - Giovanni Francesco Diotallevi
 - Irving Leander Reascos Valencia
@@ -270,12 +270,14 @@ def get_W(order):
             # (Wi_k + Wi_R) * (Wj_k + Wj_R)
             W_to_keep   = W_to_keep   + Wi_to_keep * Wj_to_keep + Wi_to_remove * Wj_to_remove
             W_to_remove = W_to_remove + Wi_to_keep * Wj_to_remove + Wi_to_remove * Wj_to_keep
+
             # -(Wi_k + Wi_R) * Ej
-            W_to_keep   = W_to_keep   - Wi_to_remove * Ej
-            W_to_remove = W_to_remove - Wi_to_keep   * Ej
+            #W_to_keep   = W_to_keep   - Wi_to_remove * Ej
+            #W_to_remove = W_to_remove - Wi_to_keep   * Ej
             # Ei * (Wj_k + Wj_R)
-            W_to_keep   = W_to_keep   + Ei * Wj_to_remove
-            W_to_remove = W_to_remove + Ei * Wj_to_keep
+            #W_to_keep   = W_to_keep   + Ei * Wj_to_remove
+            #W_to_remove = W_to_remove + Ei * Wj_to_keep
+
             # - Ei * Ej
             W_to_keep   = W_to_keep   - Ei * Ej