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
.
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.
Interface for implementing Hessian update strategies.
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.optimize
scipy.optimize._minimize.minimize
scipy.optimize._differentiable_functions.ScalarFunction
scipy.optimize._constraints.NonlinearConstraint
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