hTSrSSKrSSKrSSKrSSKJrJr /r SSjr "SS\5r g) z$Newton-CG trust-region optimization.N)_minimize_trust_regionBaseQuadraticSubproblemc jUc [S5eUcUc [S5e[X4X#UU[S.UD6$)a Minimization of scalar function of one or more variables using the Newton conjugate gradient trust-region algorithm. Options ------- initial_trust_radius : float Initial trust-region radius. max_trust_radius : float Maximum value of the trust-region radius. No steps that are longer than this value will be proposed. eta : float Trust region related acceptance stringency for proposed steps. gtol : float Gradient norm must be less than `gtol` before successful termination. zURXWU5upXZU--n X[U--n U"U 5U"U 5:aU nOU nSnX4$[R"Xf5nX- nUUU--n[RRU5U:a URXWU5upX[U--nSnX4$UUU--n[R"UU5n[R "U5U:aSnUU4$UU- nU*UU--nUnUnUnGM&)a$ Solve the subproblem using a conjugate gradient method. Parameters ---------- trust_radius : float We are allowed to wander only this far away from the origin. Returns ------- p : ndarray The proposed step. hits_boundary : bool True if the proposed step is on the boundary of the trust region. Notes ----- This is algorithm (7.2) of Nocedal and Wright 2nd edition. Only the function that computes the Hessian-vector product is required. The Hessian itself is not required, and the Hessian does not need to be positive semidefinite. g?FTr) np zeros_likerminmathsqrtjac_magr dotget_boundaries_intersectionsscipylinalgnorm)self trust_radiusp_origin tolerance hits_boundaryzrdBddBdtatbpapb p_boundary r_squaredalphaz_nextr_nextr_next_squared beta_nextd_nexts rsolveCGSteihaugSubproblem.solve,s2==*TYYt||45 D  <<) #!M* *  HH BAB&&-Cax ::1NaZaZ8d2h&!#J!#J $ !00q IOE]F||  (L8::1NaZ $ !00^FVVFF3Nyy(94 % },,&2IWy1},FAAAQrN)__name__ __module__ __qualname____firstlineno____doc__r8__static_attributes__r:rrr r *s DRrr )r:NNN) r?rnumpyr scipy.linalgr _trustregionrr__all__rr r:rrrEs-* K :>T2Tr