The update is based on the description in , p.144-146.
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
.
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 , p.143. By default uses 'auto'.
Symmetric-rank-1 Hessian update strategy.
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.optimize._minimize.minimize
scipy.optimize._constraints.NonlinearConstraint
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