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Remove printing that's specific to gradient-based VQE #262

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May 23, 2024
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Remove printing that's specific to gradient-based VQE #262

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c78741b
Remove printing that's specific to gradient-based VQE
JonathonMisiewicz May 22, 2024
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JonathonMisiewicz May 22, 2024
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Add warning
JonathonMisiewicz May 23, 2024
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39 changes: 17 additions & 22 deletions src/qforte/abc/uccvqeabc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -312,34 +312,29 @@ def report_iteration(self, x):
self._k_counter += 1

if self._k_counter == 1:
print(
"\n k iteration Energy dE Ngvec ev Ngm ev* ||g||"
)
print(
"--------------------------------------------------------------------------------------------------"
)
header = "\n k iteration Energy dE"
if self._use_analytic_grad:
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I have a suggestion for future consideration. The decision to print gradient diagnostics should ideally depend on the type of optimizer being used, whether it is gradient-based or gradient-free. Even when numerical gradients are employed, gradient diagnostics should still be printed. However, scipy.optimize.minimize does not directly expose numerical gradients to the callback function. Therefore, the current decision to report gradient diagnostics for numerical gradients is based on self._use_analytic_grad.

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Looking at uccnvqe.py, it appears that a user could potentially request optimizer = "Nelder-Mead" and use_analytic_grad = True. More or less the same applies to adaptvqe.py. To ensure accurate printing of diagnostics, I suggest internally setting self._use_analytic_grad to False if a gradient-free optimizer is selected. Additionally, a warning message could be printed to inform the user of this change.

header += " Ngvec ev Ngm ev* ||g||"
header += "\n------------------------------------------------------"
if self._use_analytic_grad:
header += "--------------------------------------------"
print(header)
if self._print_summary_file:
f = open("summary.dat", "w+", buffering=1)
f.write(
"\n# k iteration Energy dE Ngvec ev Ngm ev* ||g||"
)
f.write(
"\n#--------------------------------------------------------------------------------------------------"
)
f.close()
header.replace("\n ", "\n# ").replace("\n-", "\n#--")
with open("summary.dat", "w+", buffering=1) as f:
f.write(header)

# else:
dE = self._curr_energy - self._prev_energy
print(
f" {self._k_counter:7} {self._curr_energy:+12.10f} {dE:+12.10f} {self._res_vec_evals:4} {self._res_m_evals:6} {self._curr_grad_norm:+12.10f}"
)
update = f" {self._k_counter:7} {self._curr_energy:+12.10f} {dE:+12.10f}"
if self._use_analytic_grad:
update += f" {self._res_vec_evals:4} {self._res_m_evals:6} {self._curr_grad_norm:+12.10f}"
print(update)

if self._print_summary_file:
f = open("summary.dat", "a", buffering=1)
f.write(
f"\n {self._k_counter:7} {self._curr_energy:+12.12f} {dE:+12.12f} {self._res_vec_evals:4} {self._res_m_evals:6} {self._curr_grad_norm:+12.12f}"
)
f.close()
with open("summary.dat", "a", buffering=1) as f:
update = "\n " + update
f.write(header)

self._prev_energy = self._curr_energy

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7 changes: 7 additions & 0 deletions src/qforte/ucc/adaptvqe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -96,6 +96,13 @@ def run(
self._opt_maxiter = opt_maxiter
self._use_analytic_grad = use_analytic_grad
self._optimizer = optimizer
if self._use_analytic_grad and self._optimizer in {
"nelder-mead",
"powell",
"cobyla",
}:
print(f"{self._optimizer} optimizer doesn't support analytic grads.")
self._use_analytic_grad = False
self._pool_type = pool_type
self._use_cumulative_thresh = use_cumulative_thresh
self._add_equiv_ops = add_equiv_ops
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7 changes: 7 additions & 0 deletions src/qforte/ucc/uccnvqe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,13 @@ def run(
self._opt_maxiter = opt_maxiter
self._use_analytic_grad = use_analytic_grad
self._optimizer = optimizer
if self._use_analytic_grad and self._optimizer in {
"nelder-mead",
"powell",
"cobyla",
}:
print(f"{self._optimizer} optimizer doesn't support analytic grads.")
self._use_analytic_grad = False
self._pool_type = pool_type
self._noise_factor = noise_factor

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