o tBh~)@s~dZddlZddlZddlZddlmZmZm Z m Z ddl m Z ddl mZgZddZGd d d Z    dddZdS)zTrust-region optimization.N)_check_unknown_options_status_messageOptimizeResult_prepare_scalar_function)HessianUpdateStrategy) FD_METHODScs.dgdur dfSfdd}|fS)Nrcs*dd7<t|g|RS)Nrr)npcopy)x wrapper_argsargsfunctionncallsr/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/scipy/optimize/_trustregion.pyfunction_wrappersz(_wrap_function..function_wrapperr)rrrrr r_wrap_function s 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$r&r"prrr__call__3s*z BaseQuadraticSubproblem.__call__cC|jdur ||j|_|jS)z1Value of objective function at current iteration.N)rrrr"rrrr#6 zBaseQuadraticSubproblem.funcCr,)z=Value of Jacobian of objective function at current iteration.N)rrrr-rrrr$=r.zBaseQuadraticSubproblem.jaccCr,)zz ||\}}Wn tjjyd}Ynyw||}||}||||||}|j|j}|j|}|dkrd}nV||}|dkr|d9}n|dkr|rtd||}|| kr |}|}| r|t||dur$|t||d7}|j| kr1d}n || kr9d}n|j| kstdtdddf} | r|dkrWt| |ntd| |td|jtd|tdjtdjtdj|dt||dk||j|jjjj|d|| |d }!|dur|j |!d<| r||!d<|!S) a Minimization of scalar function of one or more variables using a trust-region algorithm. Options for the trust-region algorithm are: initial_trust_radius : float Initial trust radius. max_trust_radius : float Never propose steps that are longer than this value. eta : float Trust region related acceptance stringency for proposed steps. gtol : float Gradient norm must be less than `gtol` before successful termination. maxiter : int Maximum number of iterations to perform. disp : bool If True, print convergence message. inexact : bool Accuracy to solve subproblems. If True requires less nonlinear iterations, but more vector products. Only effective for method trust-krylov. This function is called by the `minimize` function. It is not supposed to be called directly. Nz7Jacobian is currently required for trust-region methodsz_Either the Hessian or the Hessian-vector product is currently required for trust-region methodszBA subproblem solving strategy is required for trust-region methodsrg?zinvalid acceptance stringencyz%the max trust radius must be positivez)the initial trust radius must be positivez?the initial trust radius must be less than the max trust radius)r$r%rcs||Sr)r%r()r r*rsfrrr&sz%_minimize_trust_region..hesspr3g?rsuccessmaxiterz:A bad approximation caused failure to predict improvement.z3A linalg error occurred, such as a non-psd Hessian.z Warning: z# Current function value: %fz Iterations: %dz! Function evaluations: %dz! Gradient evaluations: %dz Hessian evaluations: %d) r rTstatusr#r$nfevnjevnhevnitmessager%allvecs)r ValueError Exceptionr asarrayflattenrr#gradcallabler%r isinstancerrlenr2rFr0 LinAlgErrorminappendr rprintrWngevrYrr$)"r#x0rr$r%r& subprobleminitial_trust_radiusmax_trust_radiusetagtolrUdisp return_allcallbackinexactunknown_optionsnhesspwarnflagr<r r\mkr* hits_boundarypredicted_value x_proposed m_proposedactual_reductionpredicted_reductionrhostatus_messagesresultrrPr_minimize_trust_regionts          9     r)rNNNNrLrMrNrONFFNT)rJr6numpyr scipy.linalgr/ _optimizerrrr'scipy.optimize._hessian_update_strategyr(scipy.optimize._differentiable_functionsr__all__rrrrrrrs   X