# scipy.optimize.BFGS¶

class scipy.optimize.BFGS(exception_strategy='skip_update', min_curvature=None, init_scale='auto')[source]

Broyden-Fletcher-Goldfarb-Shanno (BFGS) Hessian update strategy.

Parameters: exception_strategy : {‘skip_update’, ‘damp_update’}, optional Define how to proceed when the curvature condition is violated. Set it to ‘skip_update’ to just skip the update. Or, alternatively, set it to ‘damp_update’ to interpolate between the actual BFGS result and the unmodified matrix. Both exceptions strategies are explained in [1], p.536-537. min_curvature : float This number, scaled by a normalization factor, defines the minimum curvature dot(delta_grad, delta_x) allowed to go unaffected by the exception strategy. By default is equal to 1e-8 when exception_strategy = 'skip_update' and equal to 0.2 when exception_strategy = 'damp_update'. init_scale : {float, ‘auto’} 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’.

Notes

The update is based on the description in [1], p.140.

References

 [1] (1, 2, 3, 4) Nocedal, Jorge, and Stephen J. Wright. “Numerical optimization” Second Edition (2006).

Methods

 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.

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