a a'@spdZddlZddlZddlZddlmZmZm Z m Z m Z ddl m Z ddlmZgZGdddZdddZdS)zTrust-region optimization.N)_check_unknown_options_wrap_function_status_messageOptimizeResult_prepare_scalar_function)HessianUpdateStrategy) FD_METHODSc@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)selfxfunjachesshessprk/Users/vegardjervell/Documents/master/model/venv/lib/python3.9/site-packages/scipy/optimize/_trustregion.py__init__sz BaseQuadraticSubproblem.__init__cCs*|jt|j|dt|||S)Ng?)rnpdotrrrprrr__call__$sz BaseQuadraticSubproblem.__call__cCs|jdur||j|_|jS)z1Value of objective function at current iteration.N)r rr rrrrr's zBaseQuadraticSubproblem.funcCs|jdur||j|_|jS)z=Value of Jacobian of objective function at current iteration.N)rrr r#rrrr.s zBaseQuadraticSubproblem.jaccCs|jdur||j|_|jS)z|t||durV|t||d7}|j| krrd}q|| krPd}qqPtdtdddf} | r|dkrt| |ntd| |td|jtd|tdjtdjtdj|dt||dk||j|jjjj|d|| |d }!|dur`|j |!d<| rn||!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)rrargscs||Sr )rr)rr!rBZsfrrrsz%_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 Warning: z# Current function value: %fz Iterations: %dz! Function evaluations: %dz! Gradient evaluations: %dz Hessian evaluations: %d) rrFstatusrrnfevZnjevnhevZnitmessagerallvecs)r ValueError ExceptionrZasarrayflattenrrZgradcallablerr isinstancerrlenr&r8r%Z LinAlgErrorminappendcopyrprintrIZngevrJrr)"rZx0rBrrrZ subproblemZinitial_trust_radiusZmax_trust_radiusetaZgtolrGZdispZ return_allcallbackZinexactZunknown_optionsZnhesspZwarnflagr0rrLmkr!Z hits_boundaryZpredicted_valueZ x_proposedZ m_proposedZactual_reductionZpredicted_reductionrhoZstatus_messagesresultrrCr_minimize_trust_regiones                  r])rNNNNr>r?r@rANFFNT)r<r*ZnumpyrZ scipy.linalgr$optimizerrrrrZ'scipy.optimize._hessian_update_strategyrZ(scipy.optimize._differentiable_functionsr __all__r r]rrrrs  X