U md)@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)xZ wrapper_argsargsfunctionZncallsT/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/scipy/optimize/_trustregion.pyfunction_wrappersz(_wrap_function..function_wrapperr)rr rrr 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_magZ _cauchy_pointZ _newton_point_fun_jac_hess_hessp)selfr funjachesshessprrr__init__'sz BaseQuadraticSubproblem.__init__cCs*|jt|j|dt|||S)Ng?)rr dotr r"rprrr__call__4sz BaseQuadraticSubproblem.__call__cCs|jdkr||j|_|jS)z1Value of objective function at current iteration.N)rrrrrrrr7s 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!r cs||Sr)r!r$)r r&r Zsfrrr"sz%_minimize_trust_region..hesspr,g?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 rJstatusrr nfevZnjevnhevnitmessager!allvecs)!r ValueError Exceptionr ZasarrayflattenrrZgradcallabler!r isinstancerrlenr+r=r*Z LinAlgErrorminappendr rprintwarningswarnRuntimeWarningrMZngevrNrr )"rZx0r r r!r"Z subproblemZinitial_trust_radiusZmax_trust_radiusetaZgtolrKZdispZ return_allcallbackZinexactZunknown_optionsZnhesspZwarnflagr5r rQmkr&Z hits_boundaryZpredicted_valueZ x_proposedZ m_proposedZactual_reductionZpredicted_reductionrhoZstatus_messagesresultrrGr_minimize_trust_regionus                   rd)rNNNNrCrDrErFNFFNT)rAr/r[numpyr Z scipy.linalgr) _optimizerrrrZ'scipy.optimize._hessian_update_strategyrZ(scipy.optimize._differentiable_functionsr__all__rrrdrrrrs,  X