+ LV-jZ.Rt^RIt^RIt^RIt^RIt^RIHtH t H t H t H t ^RI Ht^RIHt.tRt!RR4tR RltR#) zTrust-region optimization.N)_check_unknown_options_status_messageOptimizeResult_prepare_scalar_function_call_callback_maybe_halt)HessianUpdateStrategy) FD_METHODSc6aaa^.oSfSR3#VVV3RlpSV3#)Ncx<S^;;,^, uu&S!\P!V4.VS,O5!#)r )npcopy)x wrapper_argsargsfunctionncallss&*l/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/scipy/optimize/_trustregion.pyfunction_wrapper(_wrap_function..function_wrappers-q Q  ;lT&9;;)rrrrsff @r_wrap_functionrs/SFt|< # ##rca]tRt^toRtR RltRt]R4t]R4t ]R4t Rt ]R 4t R t R tR tVtR#)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``. NcWnRVnRVnRVnRVnRVnRVnW nW0nW@n WPn R#N) _x_f_g_h_g_mag _cauchy_point _newton_point_fun_jac_hess_hessp)selfrfunjachesshessps&&&&&&r__init__ BaseQuadraticSubproblem.__init__(sG !!    rcVP\P!VPV4,R\P!WP V44,,#)g?)r)r dotr*r,r(ps&&r__call__ BaseQuadraticSubproblem.__call__5s;xx"&&1--bffQ 1 6N0NNNrc vVPf!VPVP4VnVP#)z1Value of objective function at current iteration.)rr$rr(s&rr)BaseQuadraticSubproblem.fun8* 77?ii(DGwwrc vVPf!VPVP4VnVP#)z=Value of Jacobian of objective function at current iteration.)rr%rr6s&rr*BaseQuadraticSubproblem.jac?r8rc vVPf!VPVP4VnVP#)zR 24\7RS&P@R 24\7RS&PBV^,,R 24\1VV^8HVVPVPDS&P>S&P@S&PBV^,,VV$V,R7 p%VeVPV%R&V 'dXV%R&V%# \P&P(d^pEKi;i)av 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 subproblem_maxiter : int, optional Maximum number of iterations to perform per subproblem. Only affects trust-exact. Default is 25. .. versionadded:: 1.17.0 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 methodsg?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<SPV4PV4#r)r+r0)rr2rsfs&&*rr,%_minimize_trust_region..hessps771:>>!$ $rMAXITER_DEFAULTmaxiterg?)rr)successz:A bad approximation caused failure to predict improvement.z3A linalg error occurred, such as a non-psd Hessian.) stacklevelz! Current function value: fz Iterations: rLz Function evaluations: z Gradient evaluations: z Hessian evaluations: ) rrkstatusr)r*nfevnjevnhevnitmessager+allvecs)#r ValueError Exceptionr asarrayflattenrr)gradcallabler+r isinstancerrlenhasattrrCrYrA LinAlgErrorminappendr rrrprintwarningswarnRuntimeWarningrongevrqr*)'r)x0rr*r+r, subprobleminitial_trust_radiusmax_trust_radiusetagtolrjdisp return_allcallbackinexactresubproblem_maxiterunknown_optionsnhesspwarnflagrMrrtsubproblem_init_kwmkr2 hits_boundarypredicted_value x_proposed m_proposedactual_reductionpredicted_reductionrhointermediate_resultstatus_messagesresultrgs'&&&&&&&&&&&&&&&&&&, @r_minimize_trust_regionrvsRZ?+ {#$ $ | JK K01 1 OtO7881?@@q DEE/,- - B   !B " S$ B &&C ''C~~ww % *  41F G G  %JK K#5/MFEb'#+H(L A#z,--(:9%1cB/ABA A ))t    ww|4 A} A$U  CdXEWX 55:>>1eeo5 ! #H !44 : D L 4ZMq~/?@L 9AA  NN2771: & Q,qaee< $X/B C C  99t H  <H  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-Q!Q)(Q))rNNNNg?g@@g333333?g-C6?NFFNTNN)r_rGrnumpyr scipy.linalgr@ _optimizerrrrr'scipy.optimize._hessian_update_strategyr(scipy.optimize._differentiable_functionsr__all__rrrrrrrsD 33J?  $U5U5pSr