class scipy.optimize.SR1(min_denominator=1e-08, init_scale='auto')[source]

Symmetric-rank-1 Hessian update strategy.

min_denominator : float

This number, scaled by a normalization factor, defines the minimum denominator magnitude allowed in the update. When the condition is violated we skip the update. By default uses 1e-8.

init_scale : {float, ‘auto’}, optional

Matrix scale at first iteration. At the first iteration the Hessian matrix or its inverse will be initialized with init_scale*np.eye(n), where n is the problem dimension. Set it to ‘auto’ in order to use an automatic heuristic for choosing the initial scale. The heuristic is described in [1], p.143. By default uses ‘auto’.


The update is based on the description in [1], p.144-146.


[1](1, 2, 3) Nocedal, Jorge, and Stephen J. Wright. “Numerical optimization” Second Edition (2006).


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