p:i,SrSSKrSSKrSSKrSSKrSSKJrJ r J r J r J r SSK Jr SSKJr /rSr"SS 5rS S jrg) zTrust-region optimization.N)_check_unknown_options_status_messageOptimizeResult_prepare_scalar_function_call_callback_maybe_halt)HessianUpdateStrategy) FD_METHODSc4^^^S/mTcTS4$UUU4SjnTU4$)Nrc^>TS==S- ss'T"[R"U5/UT-Q76$)Nrr)npcopy)x wrapper_argsargsfunctionncallss ^/srv/projetos/modelo_ml_acdoc/venv/lib/python3.13/site-packages/scipy/optimize/_trustregion.pyfunction_wrapper(_wrap_function..function_wrappers-q Q  ;lT&9;;)rrrrs`` @r_wrap_functionrs/SFt|< # ##rcz\rSrSrSrSSjrSr\S5r\S5r \S5r S r \S 5r S r S rS rg)BaseQuadraticSubproblema9 Base/abstract class defining the quadratic model for trust-region minimization. Child classes must implement the ``solve`` method. Values of the objective function, Jacobian and Hessian (if provided) at the current iterate ``x`` are evaluated on demand and then stored as attributes ``fun``, ``jac``, ``hess``. NcXlSUlSUlSUlSUlSUlSUlX lX0lX@l XPl gN) _x_f_g_h_g_mag _cauchy_point _newton_point_fun_jac_hess_hessp)selfrfunjachesshessps r__init__ BaseQuadraticSubproblem.__init__(sG !!    rcUR[R"URU5-S[R"XR U55--$)Ng?)r+r dotr,r.r*ps r__call__ BaseQuadraticSubproblem.__call__5s;xx"&&1--bffQ 1 6N0NNNrctURc URUR5UlUR$)z1Value of objective function at current iteration.)r r&rr*s rr+BaseQuadraticSubproblem.fun8* 77?ii(DGwwrctURc URUR5UlUR$)z=Value of Jacobian of objective function at current iteration.)r!r'rr8s rr,BaseQuadraticSubproblem.jac?r:rctURc URUR5UlUR$)zS35 [5ST$R@US-S35 [/UUS:HUURURBT$R<T$R>T$R@US-UU"US9 n#UbURU#S 'U (aWU#S!'U#$![R$R&a SnGMZf=f)"a Minimization of scalar function of one or more variables using a trust-region algorithm. Options for the trust-region algorithm are: initial_trust_radius : float Initial trust radius. max_trust_radius : float Never propose steps that are longer than this value. eta : float Trust region related acceptance stringency for proposed steps. gtol : float Gradient norm must be less than `gtol` before successful termination. maxiter : int Maximum number of iterations to perform. disp : bool If True, print convergence message. inexact : bool Accuracy to solve subproblems. If True requires less nonlinear iterations, but more vector products. Only effective for method trust-krylov. workers : int, map-like callable, optional A map-like callable, such as `multiprocessing.Pool.map` for evaluating any numerical differentiation in parallel. This evaluation is carried out as ``workers(fun, iterable)``. Only for 'trust-krylov', 'trust-ncg'. .. versionadded:: 1.16.0 This function is called by the `minimize` function. It is not supposed to be called directly. Nz7Jacobian is currently required for trust-region methodsz_Either the Hessian or the Hessian-vector product is currently required for trust-region methodszBA subproblem solving strategy is required for trust-region methodsrg?zinvalid acceptance stringencyz%the max trust radius must be positivez)the initial trust radius must be positivez?the initial trust radius must be less than the max trust radius)r,r-rworkerscD>TRU5RU5$r)r-r2)rr4rsfs rr.%_minimize_trust_region..hessps771:>>!$ $rrHg?r)rr+successmaxiterz:A bad approximation caused failure to predict improvement.z3A linalg error occurred, such as a non-psd Hessian.) stacklevelz! Current function value: fz Iterations: rPz Function evaluations: z Gradient evaluations: z Hessian evaluations: ) rrmstatusr+r,nfevnjevnhevnitmessager-allvecs)"r ValueError Exceptionr asarrayflattenrr+gradcallabler-r isinstancer rlenrEr]rC LinAlgErrorminappendrrrrprintwarningswarnRuntimeWarningrrngevrtr,)%r+x0rr,r-r. subprobleminitial_trust_radiusmax_trust_radiusetagtolrndisp return_allcallbackinexactrgunknown_optionsnhesspwarnflagrQrrwmkr4 hits_boundarypredicted_value x_proposed m_proposedactual_reductionpredicted_reductionrhointermediate_resultstatus_messagesresultris% @r_minimize_trust_regionrvs7N?+ {#$ $ | JK K01 1 OtO7881?@@q DEE/,- - B   !B " S$ B &&C ''C~~ww % *  41F G G  %JK K#5/MFEb'#+H(L A#1c,A A ))t    ww|4 A} A$U  CdB 55:>>1eeo5 ! #H !44 : D L 4ZMq~/?@L 9AA  NN2771: & Q,qaee< $X/B C C  99t H  <H o ))t v I & I & H A O  q= /(+ , MM/(3^PQ R 1!%%;< %aU+, /{;< /{;< .rww/B1.EFG a(a- !155rwwRWW!#6!9!4!$3H$=?F  v#y M]yy$$ H  s<O O0/O0)rNNNNg?g@@g333333?g-C6?NFFNTN)rcrKrnumpyr scipy.linalgrB _optimizerrrrr'scipy.optimize._hessian_update_strategyr (scipy.optimize._differentiable_functionsr __all__rrrrrrrsW 33J?  $U5U5pIMADCG@E@D Hr