Source code for pysisyphus.tsoptimizers.TRIM

# [1]
#     Helgaker, 1991

import numpy as np

from scipy.optimize import newton
from pysisyphus.tsoptimizers.TSHessianOptimizer import TSHessianOptimizer

[docs] class TRIM(TSHessianOptimizer):
[docs] def optimize(self): energy, gradient, H, eigvals, eigvecs, resetted = self.housekeeping() self.update_ts_mode(eigvals, eigvecs) self.log(f"Signs of eigenvalue and -vector of root(s) {self.roots} " "will be reversed!") # Transform gradient to basis of eigenvectors gradient_ = # Construct image function by inverting the signs of the eigenvalue and # -vector of the mode to follow uphill. eigvals_ = eigvals.copy() eigvals_[self.roots] *= -1 gradient_ = gradient_.copy() gradient_[self.roots] *= -1 def get_step(mu): zetas = -gradient_ / (eigvals_ - mu) # Replace nan with 0. zetas = np.nan_to_num(zetas) # Transform to original basis step = eigvecs * zetas step = step.sum(axis=1) return step def get_step_norm(mu): return np.linalg.norm(get_step(mu)) def func(mu): return get_step_norm(mu) - self.trust_radius mu = 0 norm0 = get_step_norm(mu) if norm0 > self.trust_radius: mu, res = newton(func, x0=mu, full_output=True) assert res.converged self.log(f"Using levelshift of μ={mu:.4f}") else: self.log("Took pure newton step without levelshift") step = get_step(mu) step_norm = np.linalg.norm(step) self.log(f"norm(step)={step_norm:.6f}") self.predicted_energy_changes.append(self.quadratic_model(gradient, self.H, step)) return step