Update descent.py

src/seemps/optimization/descent.py

@@ -112,24 +112,18 @@ def energy_and_variance(state: MPS) -> tuple[MPS, float, float, float]:
     state = CanonicalMPS(state, normalize=True)
     for step in range(maxiter):
         H_state, E, variance, avg_H2 = energy_and_variance(state)
+        if callback is not None:
+            callback(
+            state
+            ),
+        )
         if DEBUG:
             log(f"step = {step:5d}, energy = {E}, variance = {variance}")
         energies.append(E)
         variances.append(variance)
         if E < best_energy:
             best_energy, best_vector, _ = E, state, variance
-        if callback is not None:
-            callback(
-            state,
-            OptimizeResults(
-                state=best_vector,
-                energy=best_energy,
-                converged=converged,
-                message=message,
-                trajectory=energies,
-                variances=variances,
-            ),
-        )
+
         E_mean: float = np.mean(energies[(-min(k_mean, len(energies)-1))-1 : -1])  # type: ignore        
         if (E_mean - last_E_mean > 0 and E_mean - last_E_mean >= abs(tol_up)) or (
             E_mean - last_E_mean < 0 and E_mean - last_E_mean >= -abs(tol)
@@ -154,22 +148,15 @@ def energy_and_variance(state: MPS) -> tuple[MPS, float, float, float]:
         # which was already calculated
     if not converged:
         H_state, E, variance, _ = energy_and_variance(state)
-        if E < best_energy:
-            best_energy, best_vector, _ = E, state, variance
-        energies.append(E)
-        variances.append(variance)
         if callback is not None:
             callback(
                 state,
-                OptimizeResults(
-                    state=best_vector,
-                    energy=best_energy,
-                    converged=converged,
-                    message=message,
-                    trajectory=energies,
-                    variances=variances,
                 ),
             )
+        if E < best_energy:
+            best_energy, best_vector, _ = E, state, variance
+        energies.append(E)
+        variances.append(variance)
     return OptimizeResults(
         state=best_vector,
         energy=best_energy,