o fRh1*@sdZddlZddlZddlZddlZddlmZm Z m Z m Z m Z ddl mZddlmZgZddZGd d d Z    dddZdS)zTrust-region optimization.N)_check_unknown_options_status_messageOptimizeResult_prepare_scalar_function_call_callback_maybe_halt)HessianUpdateStrategy) FD_METHODScs.dgdur dfSfdd}|fS)Nrcs*dd7<t|g|RS)Nrr)npcopy)x wrapper_argsargsfunctionncalls]/home/air/sanwanet/backup_V2/venv/lib/python3.10/site-packages/scipy/optimize/_trustregion.pyfunction_wrappersz(_wrap_function..function_wrapperr)rrrrrr_wrap_functions rc@sjeZdZdZdddZddZeddZed d Zed d Z d dZ eddZ ddZ ddZ dS)BaseQuadraticSubproblemaQ 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``. NcCsF||_d|_d|_d|_d|_d|_d|_||_||_||_ ||_ dSN) _x_f_g_h_g_mag _cauchy_point _newton_point_fun_jac_hess_hessp)selfr funjachesshessprrr__init__(s z BaseQuadraticSubproblem.__init__cCs*|jt|j|dt|||S)Ng?)r$r dotr%r'r#prrr__call__5s*z BaseQuadraticSubproblem.__call__cC|jdur ||j|_|jS)z1Value of objective function at current iteration.N)rrrr#rrrr$8 zBaseQuadraticSubproblem.funcCr-)z=Value of Jacobian of objective function at current iteration.N)rr rr.rrrr%?r/zBaseQuadraticSubproblem.jaccCr-)z.hesspr4g?r)r r$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: %dz! Function evaluations: %dz! Gradient evaluations: %dz Hessian evaluations: %d) r rUstatusr$r%nfevnjevnhevnitmessager&allvecs)"r ValueError Exceptionr asarrayflattenrr$gradcallabler&r isinstancerrlenr3rGr1 LinAlgErrorminappendr rrrprintwarningswarnRuntimeWarningrZngevr\r%)#r$x0rr%r&r' subprobleminitial_trust_radiusmax_trust_radiusetagtolrVdisp return_allcallbackinexactunknown_optionsnhesspwarnflagr=r r_mkr+ hits_boundarypredicted_value x_proposed m_proposedactual_reductionpredicted_reductionrhointermediate_resultstatus_messagesresultrrQr_minimize_trust_regionvs          ;     r)rNNNNrMrNrOrPNFFNT)rKr7rlnumpyr scipy.linalgr0 _optimizerrrrr'scipy.optimize._hessian_update_strategyr(scipy.optimize._differentiable_functionsr __all__rrrrrrrs"  X