^Mh ddlmZddlmZddlZddlZddlmZm Z m Z m Z ddl m Z ddlmZmZddlmZmZdd lmZGd d ZGd d ZGddZGddZGddZdS))suppress) signatureN)BoundsLinearConstraintNonlinearConstraintOptimizeResult)PreparedConstraint) PRINT_OPTIONSBARRIER)CallbackSuccessget_arrays_tol)exact_1d_arraycJeZdZdZdZdZedZedZdS)ObjectiveFunctionz) Real-valued objective function. c|rA|t|sJt|tsJt|tsJ||_||_||_d|_dS)a Initialize the objective function. Parameters ---------- fun : {callable, None} Function to evaluate, or None. ``fun(x, *args) -> float`` where ``x`` is an array with shape (n,) and `args` is a tuple. verbose : bool Whether to print the function evaluations. debug : bool Whether to make debugging tests during the execution. *args : tuple Additional arguments to be passed to the function. Nr)callable isinstancebool_fun_verbose_args_n_eval)selffunverbosedebugargss Y/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/scipy/_lib/cobyqa/problem.py__init__zObjectiveFunction.__init__sk&  +;(3--;;;gt,, , , ,eT** * * *    cvtj|t}|jd}nttj|j|g|jR}|xjdz c_|jrJtjdit5t|j d|d|dddn #1swxYwY|S)a Evaluate the objective function. Parameters ---------- x : array_like, shape (n,) Point at which the objective function is evaluated. Returns ------- float Function value at `x`. dtypeNr () = ) nparrayfloatrsqueezerrr printoptionsr printname)rxfs r__call__zObjectiveFunction.__call__6s HQe $ $ $ 9 AAbj1!:tz!:!:!:;;<r?r@r rArJrLr]r[rar(r!rrCrCnsGGG4  X   X !!!")))GGGGGr!rCceZdZdZdZedZedZedZedZ edZ edZ d Z d Z d S) LinearConstraintszK Linear constraints ``a_ub @ x <= b_ub`` and ``a_eq @ x == b_eq``. c|rJt|tsJ|D]}t|tsJt|tsJt jdf|_t jd|_t jdf|_t jd|_ |D]C}t j |j |j z t|j |j k}t j|rgt j|j|j|f|_t j|jd|j ||j |zzf|_ t j|stt j|j|j||j| f|_t j|j|j ||j | f|_Ed|jt j|j<d|jt j|j<t j|jt j|jz}t j|j}|j|ddf|_|j||_|j|ddf|_|j||_ fd|D|_dS)a2 Initialize the linear constraints. Parameters ---------- constraints : list of LinearConstraint Linear constraints. n : int Number of variables. debug : bool Whether to make debugging tests during the execution. r?r%Nclg|]0}|jjt|tj1Sr()ArRr r)rQ).0cns r z.LinearConstraints.__init__..sD   23ach q"'!** - -   r!)rlistrrr)empty_a_ub_b_ub_a_eq_b_eqabsrGrEranyvstacka_eqrg concatenateb_eqrMa_ubb_ubrKisinfrS)r constraintsrjr constraint is_equalityundef_ubundef_eqs ` rr zLinearConstraints.__init__s  +k400 0 0 0) @ @ !*.>??????eT** * * *Xq!f%% Xa[[ Xq!f%% Xa[[ %  J& - z}==>Kvk"" Y :< 3L'MNN ^ &M+6(mK89   6+&& Y " k\2#{l33  ^ " {l3# |44 *- "(49%%&), "(49%%&8DI&&$))<)<<8DI&&Yy!!!|, Yy) Yy!!!|, Yy)     7B   r!c|jS)z Left-hand side matrix of the linear inequality constraints. Returns ------- `numpy.ndarray`, shape (m, n) Left-hand side matrix of the linear inequality constraints. )rnr6s rrxzLinearConstraints.a_ub zr!c|jS)z Right-hand side vector of the linear inequality constraints. Returns ------- `numpy.ndarray`, shape (m, n) Right-hand side vector of the linear inequality constraints. )ror6s rryzLinearConstraints.b_ub#rr!c|jS)z Left-hand side matrix of the linear equality constraints. Returns ------- `numpy.ndarray`, shape (m, n) Left-hand side matrix of the linear equality constraints. )rpr6s rruzLinearConstraints.a_eq/rr!c|jS)z Right-hand side vector of the linear equality constraints. Returns ------- `numpy.ndarray`, shape (m, n) Right-hand side vector of the linear equality constraints. )rqr6s rrwzLinearConstraints.b_eq;rr!c|jjSz Number of linear inequality constraints. Returns ------- int Number of linear inequality constraints. )ryrRr6s rm_ubzLinearConstraints.m_ubGy~r!c|jjSz Number of linear equality constraints. Returns ------- int Number of linear equality constraints. )rwrRr6s rm_eqzLinearConstraints.m_eqSrr!cTtj||dS)rYr%initialr)maxr[r\s rr]zLinearConstraints.maxcv_s%vdnnQ''5555r!ct|jr%tjfd|jDStjgS)Nc:g|]}|Sr(r[rhpcr0s rrkz/LinearConstraints.violation..qs#"F"F"Fr2<<??"F"F"Fr!)lenrSr)rvr*r\s `rr[zLinearConstraints.violationosI tx== H>"F"F"F"FTX"F"F"FGG Gx||r!N)r<r>r?r@r rArxryrurwrrr]r[r(r!rrcrcsB B B H  X   X   X   X   X   X 666 r!rccpeZdZdZdZdZedZedZedZ d dZ d d Z dS) NonlinearConstraintszI Nonlinear constraints ``c_ub(x) <= 0`` and ``c_eq(x) == b_eq``. c.|rat|tsJ|D]}t|tsJt|tsJt|tsJ||_g|_||_d|_d|_dx|_ |_ dS)aA Initialize the nonlinear constraints. Parameters ---------- constraints : list Nonlinear constraints. verbose : bool Whether to print the function evaluations. debug : bool Whether to make debugging tests during the execution. N) rrlrr _constraintsrSr_map_ub_map_eq_m_ub_m_eq)rr{rrr|s rr zNonlinearConstraints.__init__zs  +k400 0 0 0) C C !*.ABBBBBBgt,, , , ,eT** * * *'   "&& TZZZr!c t|js6dx|_|_t jgt jgfSt j|t }t|jsg|_g|_ d|_d|_|jD]k}t|j s5tj |}d|_ d|_ t||}nt||}d|j_|j|t j|jj}|jd|jd}}t+||}t j||z |k} |j || |j|| |xjt j| z c_|xjt j| z c_mg} g} t1|jD]\} }|j|} |jrt jd it65t9t:5|j| jj}t?|d|d| d d d n #1swxYwYd d d n #1swxYwY|j | }|j| }| |}t|r|jd|}|jd|}|tj k}||||z }| ||tj k}||||z }| || |}t|r/d |jd||jd|zz}||z}| ||jrt j!| } nt jg} |jrt j!| } nt jg} | j"|_| j"|_| | fS) a Calculates the residual (slack) for the constraints. Parameters ---------- x : array_like, shape (n,) Point at which the constraints are evaluated. Returns ------- `numpy.ndarray`, shape (m_nonlinear_ub,) Nonlinear inequality constraint slack values. `numpy.ndarray`, shape (m_nonlinear_eq,) Nonlinear equality constraint slack values. rr#c|SNr()x0s rz/NonlinearConstraints.__call__..srr!cdS)Nr%r()rvs rrz/NonlinearConstraints.__call__..s3r!Tr r&r'Nrer()#rrrrr)r*r+rSrrrjaccopyhessr r f_updatedappendarangerPrTrrrrO enumeraterr-r rr=r<r.rIrvrR)rr0r|riridxrErGarr_tolr}c_ubc_eqivalfun_nameeq_idxub_idxub_valrJrL finite_xl_v finite_xueq_valmidpoints rr2zNonlinearConstraints.__call__s 4$%% .&' 'DJ8B<<"- - HQe $ $ $48}}! =DLDLDJDJ"/ = =  // ; *--A)MAE..AF+Aq11BB+J::B$( ###i))1ry|B(R00 fR"Woo8  ##C $4555 ##C $5666 b.{;;;  b. |<<< tx(( EAr&**Q--C} :_55}55::!.11::#'#4Q#7#;#D88A88388999:::::::::::::::::::::::::::::: \!_F\!_F[F6{{ Yq\&)Yq\&)"&L  ]VI%66 BK I&I6 B[F6{{ #")A,v"61f9M"MN(" KK     : >$''DD8B<$''DD8B<s4(v NN1gwN ? ?    r!cNtjfd|jDS)Nc:g|]}|Sr(rrs rrkz2NonlinearConstraints.violation..Ws#BBB2r||ABBBr!)r)rvrSrs ` rr[zNonlinearConstraints.violationVs*~BBBBBBBCCCr!NN) r<r>r?r@r r2rArrr7r]r[r(r!rrrus''':jjjXX*X*  X     0DDDDDDr!rceZdZdZdZddZedZedZedZ edZ ed Z ed Z ed Z ed Zed ZedZedZedZedZedZedZedZdZddZddZdZdS)Problemz Optimization problem. c  | rt|tsJt|tsJt|tsJt|tsJt|t sJt|t sJt| t sJt| tsJ| r| dksJt| tsJ| dksJt| t sJ||_||_ ||_ |t|std||_ t|d}|j} |jj| krt#d| d|jjd| krt#d| d t)|j|j}|j|jkt-j|j|jz |kz|_d |j|j|j|jzz|_t-j|j|j|j|j|j|_||_tt9|j|j|j|j|_|j||j|_|j |j!dd|jf|jzz }ttE|jdd|jft,j# |j$|jdd|jf|jzz tE|j!dd|jf||g|j%| |_ |ok|jj&o_t-j't-j(|jjo/t-j't-j(|jj}|r~d |jj|jjz z|_)d |jj|jjzz|_*tt9t-j+|j% t-j+|j%|_|j j |j j!|j*zz }ttE|j jt-j,|j)zt,j# |j j$|j j|j*zz tE|j j!t-j,|j)z||g|j%| |_ |j|j*z |j)z |_n> > > >ou55 5 5 5eT** * * *mT22 2 2 2lC00 0 0 0 (#a''''k3// / / /????eT** * * *  #  H%% M KLLL!B E F F G 9>Q  BQBBBCC C ; Q 1 $ $%%%%  VY 22!9 1 F69vy( ) )C /  Ido &4?)C C ' O Ido & Ido &  #' 69do-. 4?:J0K L L   <''DO+;(<=={V[DO);t>|tj|<t>|tj|<tAtCt>t> tj"tj#|t>t> }tj"tj#|t>t> }||fS) a Evaluate the objective and nonlinear constraint functions. Parameters ---------- x : array_like, shape (n,) Point at which the functions are evaluated. penalty : float, optional Penalty parameter used to select the point in the filter to forward to the callback function. Returns ------- float Objective function value. `numpy.ndarray`, shape (m_nonlinear_ub,) Nonlinear inequality constraint function values. `numpy.ndarray`, shape (m_nonlinear_eq,) Nonlinear equality constraint function values. Raises ------ `cobyqa.utils.CallbackSuccess` If the callback function raises a ``StopIteration``. r#rc3zK|]5\}}tj|r|kptj|V6dSrr)rK)rh fun_filter maxcv_filter maxcv_vals r z#Problem.__call__..7sf  -J $$- ,*8L))      r!c3zK|]5\}}tj|r|kptj|V6dSrr)rhrrfun_vals rrz#Problem.__call__..Asf  -J &&)j((8J''      r!c36K|]\}}|kp|kVdSrr()rhrrrrs rrz#Problem.__call__..KsJ  ,J *$@ L(@      r!Nintermediate_result)r0r)r)$r)rZr+build_xrrr]rrrrrrrpoprKrrMziprrrangerrr best_evalset parametersr StopIterationr r rminmaximumminimum)rr0penaltyx_fullrr include_pointk remove_pointsigx_bestfun_best_rexcrrs @@rr2zProblem.__call__ s6 Jq & & &a))F##??622JJq'733   '   $ $W - - -   & &y 1 1 1 O " "1 % % %4$%%(:::!%%a(((#''***##A&&& 8G   )!4!4  011Q6MM Xg       14$&11    MMXi      14$&11    MM      03$&11   M  &   # #G , , ,   % %i 0 0 0 N ! !! $ $ $ 3t/0014b"== * *8G$$ #%8D,q,A#B#BLL!1!!455?8D$6q$9::?"d&6q&99?%);A)>> !  *$((+++&**1---N&&q)))4#$$t'888 $$Q'''"&&q)))""1%%% > %DN++C /&*nnW&=&=#!f--s~&&+@*AAA*8 $+++' NN7JNKKKKNN6***  / / /%3. / 8G   G%,!!"%,!!"c'7++gX66*RZ99G8DD*RZ99G8DD((sBS S3&S..S3c|jjS)zt Number of variables. Returns ------- int Number of variables. )rrRr6s rrjz Problem.nsw|r!c|jjS)z Number of variables in the original problem (with fixed variables). Returns ------- int Number of variables in the original problem (with fixed variables). )rrRr6s rn_origzProblem.n_origs##r!c|jS)z Initial guess. Returns ------- `numpy.ndarray`, shape (n,) Initial guess. )rr6s rrz Problem.x0rVr!c|jjSr4)rr7r6s rr7zProblem.n_evalsyr!c|jjS)r:)rr/r6s rrzProblem.fun_namerr!c|jS)z} Bound constraints. Returns ------- BoundConstraints Bound constraints. )rr6s rrTzProblem.boundsr8r!c|jS)z Linear constraints. Returns ------- LinearConstraints Linear constraints. )rr6s rrzProblem.linearr8r!c|jjS)z Number of bound constraints. Returns ------- int Number of bound constraints. )rTrPr6s rm_boundszProblem.m_boundss{}r!c|jjSr)rrr6s r m_linear_ubzProblem.m_linear_ub{r!c|jjSr)rrr6s r m_linear_eqzProblem.m_linear_eqrr!c|jjS)a  Number of nonlinear inequality constraints. Returns ------- int Number of nonlinear inequality constraints. Raises ------ ValueError If the number of nonlinear inequality constraints is not known. )rrr6s rm_nonlinear_ubzProblem.m_nonlinear_ub##r!c|jjS)a Number of nonlinear equality constraints. Returns ------- int Number of nonlinear equality constraints. Raises ------ ValueError If the number of nonlinear equality constraints is not known. )rrr6s rm_nonlinear_eqzProblem.m_nonlinear_eqr$r!cBtj|jtS)z History of objective function evaluations. Returns ------- `numpy.ndarray`, shape (n_eval,) History of objective function evaluations. r#)r)r*rr+r6s r fun_historyzProblem.fun_history(sx)7777r!cBtj|jtS)z History of maximum constraint violations. Returns ------- `numpy.ndarray`, shape (n_eval,) History of maximum constraint violations. r#)r)r*rr+r6s r maxcv_historyzProblem.maxcv_history4sx+59999r!c |jdks |jdkrdS|jdks |jdkrdS|jdkrdSdS#t $rYdSwxYw)z Type of the problem. The problem can be either 'unconstrained', 'bound-constrained', 'linearly constrained', or 'nonlinearly constrained'. Returns ------- str Type of the problem. rznonlinearly constrainedzlinearly constrainedzbound-constrained unconstrained)r#r&rr!rrr6s rtypez Problem.type@s -"Q&&$*=*A*A00!A%%)9A)=)=--""**& - - - -,,  -sAA A AAc|jdkS)z Whether the problem is a feasibility problem. Returns ------- bool Whether the problem is a feasibility problem. r;)rr6s ris_feasibilityzProblem.is_feasibility^s}""r!ctj|j}|j||j<||jz|jz||j<|j|S)a0 Build the full vector of variables from the reduced vector. Parameters ---------- x : array_like, shape (n,) Reduced vector of variables. Returns ------- `numpy.ndarray`, shape (n_orig,) Full vector of variables. ) r)rmrrrrrrra)rr0r s rrzProblem.build_xjs\$+&&"&/t$%(<$<&*&9%:  ((000r!Nc||||}tj|rtj|dSdSr)r[r)rOr)rr0rrr[s rr]z Problem.maxcv~sH(NN1gwNGG I & & 6)S111 13r!cg}|jjs/|j|}||t |jjr/|j|}||t |jjr1|j|||}||t |rtj |SdSr) rTrNr[rrrrSrr)rv)rr0rrr[blcnlcs rr[zProblem.violations {&  %%a((A   Q    t{   !&&q))B   R t" # # "/++Aw@@C   S ! ! ! y>> ->),, , - -r!ct|jdkr||jtj|j}tj|j}tj|j}tj|}tj|r&||j k}tj|rtj tj ||sv||tj ||kz}tj |dkr!||tj||kz}tj|d}ntj|rtj|d}ntj|tj} |||||zz| |<tj tj | r4|tj |k} tj| d}n| tj | k} tj | dkr!| |tj|| kz} tj | dkr!| |tj|| kz} tj| d}nktj tj |s3|tj |k}tj|d}nt|dz }|j||ddf||||fS)a Return the best point in the filter and the corresponding objective and nonlinear constraint function evaluations. Parameters ---------- penalty : float Penalty parameter Returns ------- `numpy.ndarray`, shape (n,) Best point. float Corresponding objective function value. float Corresponding maximum constraint violation. rr rN)rrrr)r*rrrrsrrMrKnanminrOr flatnonzero full_likenanrTra) rr rrx_filter finite_idx feasible_idx fun_min_idxr merit_filter min_maxcv_idx merit_min_idxs rrzProblem.best_evals%, t A % % DMMMXd.// x 233 8DN++[.. 6*  E $'4+@@Lvl##7 :BFL122--7 :+")J|,D"E"EE #K00144<26$[144$KN;//3 %%' :N<004 "|J?? z*W|J7O-OOZ(6"(<0011:%1BIl4K4K$KM}55b9AA%1BIl4K4K$KM' 66::%(7::* ' 66::%rv&}588* }55b9AA,,-- $ % *(=(==K{++B/AAJ!#A K  AAA / / qM O  r!)r%r)r<r>r?r@r r2rArjrrr7rrTrrrr!r#r&r(r*r-r/rr]r[rr(r!rrrZsEiiiVB)B)B)B)H  X  $ $X $  X    X    X   X   X   X    X     X  $$X$ $$X$  8 8X 8 : :X :--X-: # #X #111(4---- h h h h h r!r) contextlibrinspectrrnumpyr)scipy.optimizerrrrscipy.optimize._constraintsr settingsr r utilsr rrrrCrcrrr(r!rrIs  ;:::::-,,,,,,,22222222!!!!!!WWWWWWWWt]G]G]G]G]G]G]G]G@ddddddddNbDbDbDbDbDbDbDbDJv  v  v  v  v  v  v  v  v  v  r!