OptimizationAlgorithm#

class optking.stepAlgorithms.OptimizationAlgorithm(molsys, history, params)[source]#

Bases: OptimizationInterface

The standard minimization and transition state algorithms inherit from here. Defines the take_step for those algorithms. Backstep and trust radius management are performed here.

All child classes implement a step() method using the forces and Hessian to compute a step direction and possibly a step_length. trust_radius_on = False allows a child class to override a basic trust radius enforcement.

Methods Summary

`apply_interfrag_step_scaling`(dq)	Check the size of the interfragment modes.
`apply_intrafrag_step_scaling`(dq)	Apply maximum step limit by scaling.
`assess_previous_step`()	Determine whether the last step was acceptable, prints summary and change trust radius
`backstep`()	takes a partial step backwards.
`backstep_needed`(fq)	Simple logic for whether a backstep is advisable (or too many have been taken).
`converged`(dq, fq, step_number[, str_mode])
`decrease_trust_radius`()	Scale trust radius by 0.25
`expected_energy`(dq, fq, H)	Compute the expected energy given the model for the step
`increase_trust_radius`()	Increase trust radius by factor of 3
`requires`(**kwargs)	Returns tuple with strings ('energy', 'gradient', 'hessian') for what the algorithm needs to compute a new point
`step`(fq, H, args, *kwargs)	Basic form of the algorithm
`supports_trust_region`()	Returns boolean for whether a trust region should be used with this method
`take_step`([fq, H, energy, return_str])	Compute step and take step
`update_history`(delta_e, achieved_dq, ...)	Basic history update method.

Methods Documentation

apply_interfrag_step_scaling(dq)[source]#

Check the size of the interfragment modes. They can inadvertently represent very large motions.

Returns:: dq
Return type:: scaled step according to trust radius

apply_intrafrag_step_scaling(dq)[source]#: Apply maximum step limit by scaling.

assess_previous_step()[source]#: Determine whether the last step was acceptable, prints summary and change trust radius

backstep()[source]#

takes a partial step backwards. fq and H should correspond to the previous not current point

Notes

Take partial backward step. Update current step in history. Divide the last step size by 1/2 and displace from old geometry.

backstep_needed(fq)[source]#

Simple logic for whether a backstep is advisable (or too many have been taken).

Returns:: bool
Return type:: True if a backstep should be taken

converged(dq, fq, step_number, str_mode='', **kwargs)[source]#

decrease_trust_radius()[source]#: Scale trust radius by 0.25

expected_energy(dq, fq, H)[source]#

Compute the expected energy given the model for the step

Parameters:

step (float) – normalized step (unit length)
grad (np.ndarray) – projection of gradient onto step
hess (np.ndarray) – projection of hessian onto step

increase_trust_radius()[source]#: Increase trust radius by factor of 3

abstract requires(**kwargs) → tuple[source]#: Returns tuple with strings (‘energy’, ‘gradient’, ‘hessian’) for what the algorithm needs to compute a new point

abstract step(fq: ndarray, H: ndarray, *args, **kwargs) → ndarray[source]#: Basic form of the algorithm

abstract supports_trust_region() → bool[source]#: Returns boolean for whether a trust region should be used with this method

take_step(fq=None, H=None, energy=None, return_str=False, **kwargs) → tuple[ndarray, str] | ndarray[source]#: Compute step and take step

update_history(delta_e, achieved_dq, unit_dq, projected_f, projected_hess)[source]#: Basic history update method. This should be expanded here and in child classes in future