Hessian Update Strategies¶
This module provides various generic Hessian approximation strategies that can be employed when the calculating the exact Hessian or an approximation is computationally too demandind.
Classes Summary
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Broyden-Fletcher-Goldfarb-Shanno update strategy. |
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Davidon-Fletcher-Powell update strategy. |
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Abstract class from which Hessian update strategies should subclass |
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Symmetric Rank 1 update strategy. |
Classes
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class
fides.hessian_approximation.
BFGS
(hess_init=None)[source]¶ Broyden-Fletcher-Goldfarb-Shanno update strategy. This is a rank 2 update strategy that always yields positive-semidefinite hessian approximations.
-
class
fides.hessian_approximation.
DFP
(hess_init=None)[source]¶ Davidon-Fletcher-Powell update strategy. This is a rank 2 update strategy that always yields positive-semidefinite hessian approximations. It usually does not perform as well as the BFGS strategy, but included for the sake of completeness.
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class
fides.hessian_approximation.
HessianApproximation
(hess_init=None)[source]¶ Abstract class from which Hessian update strategies should subclass
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__init__
(hess_init=None)[source]¶ Creata Hessian update strategy instance
- Parameters
hess_init (
typing.Optional
[numpy.ndarray
]) – Inital guess for the Hessian, if empty Identity matrix will be used
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get_mat
()[source]¶ Getter for the Hessian approximation :rtype:
numpy.ndarray
:return:
-