scipy.optimize.

HessianUpdateStrategy

class scipy.optimize.HessianUpdateStrategy

Interface for implementing Hessian update strategies.

Many optimization methods make use of Hessian (or inverse Hessian) approximations, such as the quasi-Newton methods BFGS, SR1, L-BFGS. Some of these approximations, however, do not actually need to store the entire matrix or can compute the internal matrix product with a given vector in a very efficiently manner. This class serves as an abstract interface between the optimization algorithm and the quasi-Newton update strategies, giving freedom of implementation to store and update the internal matrix as efficiently as possible. Different choices of initialization and update procedure will result in different quasi-Newton strategies.

Four methods should be implemented in derived classes: initialize, update, dot and get_matrix.

Methods

dot(p)	Compute the product of the internal matrix with the given vector.
get_matrix()	Return current internal matrix.
initialize(n, approx_type)	Initialize internal matrix.
update(delta_x, delta_grad)	Update internal matrix.

Notes

Any instance of a class that implements this interface, can be accepted by the method minimize and used by the compatible solvers to approximate the Hessian (or inverse Hessian) used by the optimization algorithms.