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HessianApproximationBFGS

Source: Distributed_Design_Optimizer/subsystem/tools/HessianApproximationBFGS.py

BFGS Hessian approximation module.

This module provides BFGS-based Hessian approximation for optimization algorithms.

Classes

HessianApproximationBFGS

Inherits from: BFGS

A HessianApproximationBFGS object extends the scipy BFGS Hessian update strategy.

It tracks previous design variables and gradients, enabling incremental Hessian approximation updates from absolute (current) values rather than deltas.

Args:

exception_strategy (str): Strategy when curvature condition is violated
('skip_update' or 'damp_update').
min_curvature (float | None): Minimum curvature threshold.
init_scale (float | str): Initialization scale for the Hessian matrix.

Methods

init(self, exception_strategy: str, min_curvature: float | None, init_scale: float | str) → None

Creates a new instance of HessianApproximationBFGS.

Args:

exception_strategy (str): Strategy when curvature condition is violated
('skip_update' or 'damp_update').
min_curvature (float | None): Minimum curvature threshold.
init_scale (float | str): Initialization scale for the Hessian matrix.

get_Previous_X(self) → List[float] | None

Returns the previous design variable vector.

Returns:

List[float] | None: the previous design variable vector

set_Previous_X(self, previous_x_in: List[float]) → None

Sets the previous design variable vector.

Args:

previous_x_in (List[float]): the previous design variable vector

get_Previous_G(self) → List[float] | None

Returns the previous gradient vector.

Returns:

List[float] | None: the previous gradient vector

set_Previous_G(self, previous_g_in: List[float]) → None

Sets the previous gradient vector.

Args:

previous_g_in (List[float]): the previous gradient vector

check_CurvatureConditionViolation(self, delta_x: List[float], delta_g: List[float]) → bool

Check if the curvature condition of BFGS is triggered.

This also happens in the function update of scipy.optimize.BFGS. The logic is based on the check in _update_implementation in scipy.optimize.BFGS. Source: https://github.com/scipy/scipy/blob/v1.17.0/scipy/optimize/_hessian_update_strategy.py, L379-L422

Args:

delta_x (List[float]): Change in design variables.
delta_g (List[float]): Change in gradient.

Returns:

bool: True if the curvature condition is violated, False otherwise.

updateHessianApproximation(self, current_x: List[float], current_g: List[float]) → None

Update the internal BFGS Hessian approximation using the current design variables and gradient.

Computes the deltas from the stored previous values and delegates to the parent BFGS update method.

After the update, the previous design variables and gradient are overwritten with the current values.

Args:

current_x (List[float]): the current design variable vector
current_g (List[float]): the current gradient vector

update_state(self, other_hessian_approximator: HessianApproximationBFGS) → None

Updates the self object with the data from other_hessian_approximator.

Args:

other_hessian_approximator (HessianApproximationBFGS): The source object to copy state from.