U |e)@s|dZddlZddlZddlZddlZddlmZm Z m Z m Z ddl m Z ddlmZgZddZGd d d ZdddZdS)zTrust-region optimization.N)_check_unknown_options_status_messageOptimizeResult_prepare_scalar_function)HessianUpdateStrategy) FD_METHODScs.dgdkrdfSfdd}|fS)Nrcs(dd7<t|f|S)Nrr)npcopy)x wrapper_argsargsfunctionncallsX/var/www/website-v5/atlas_env/lib/python3.8/site-packages/scipy/optimize/_trustregion.pyfunction_wrappersz(_wrap_function..function_wrapperr)rrrrr r_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__'sz BaseQuadraticSubproblem.__init__cCs*|jt|j|dt|||S)Ng?)r#r dotr$r&r"prrr__call__4sz BaseQuadraticSubproblem.__call__cCs|jdkr||j|_|jS)z1Value of objective function at current iteration.N)rrrr"rrrr#7s zBaseQuadraticSubproblem.funcCs|jdkr||j|_|jS)z=Value of Jacobian of objective function at current iteration.N)rrrr,rrrr$>s zBaseQuadraticSubproblem.jaccCs|jdkr||j|_|jS)z|t||dk rV|t||d7}|j| krrd}q|| krPd}qqPtdtdddf} | r|dkrt| |nt| |tdtd|jtd|tdjtdjtdj|dt||dk||j|j jjj|d|| |d }!|dk rb|j |!d<| rp||!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$r%rcs||Sr)r%r()r r*rsfrrr&sz%_minimize_trust_region..hesspr1g?rsuccessmaxiterz:A bad approximation caused failure to predict improvement.z3A linalg error occurred, such as a non-psd Hessian.z# Current function value: %fz Iterations: %dz! Function evaluations: %dz! Gradient evaluations: %dz Hessian evaluations: %d) r rQstatusr#r$nfevnjevnhevnitmessager%allvecs)!r ValueError Exceptionr asarrayflattenrr#gradcallabler%r isinstancerrlenr0rCr. LinAlgErrorminappendr rprintwarningswarnRuntimeWarningrTngevrVrr$)"r#x0rr$r%r& subproblemZinitial_trust_radiusZmax_trust_radiusetagtolrRdisp return_allcallbackinexactunknown_optionsZnhesspwarnflagr:r rYmkr* hits_boundaryZpredicted_valueZ x_proposedZ m_proposedZactual_reductionZpredicted_reductionrhoZstatus_messagesresultrrMr_minimize_trust_regionus                   ry)rNNNNrIrJrKrLNFFNT)rGr4rfnumpyr scipy.linalgr- _optimizerrrrZ'scipy.optimize._hessian_update_strategyr(scipy.optimize._differentiable_functionsr__all__rrryrrrrs,  X