cand_xs = np.array([r[0] for r in results])
cand_acqs = np.array([r[1] for r in results])
best_ind = np.argmin(cand_acqs)
a = cand_acqs[best_ind]
if a < best:
next_x, best = cand_xs[best_ind], a
After Change
cand_xs = np.array([r[0] for r in results])
cand_acqs = np.array([r[1] for r in results])
next_x = cand_xs[np.argmin(cand_acqs)]
// lbfgs should handle this but just in case there are precision errors.
next_x = np.clip(
next_x, transformed_bounds[:, 0], transformed_bounds[:, 1])
