(ph SSKJr SSKJr SSKrSSKrSSKJrJ r J r J r SSK J r SSKJrJr SSKJrJr SS KJr "S S 5r"S S 5r"SS5r"SS5r"SS5rg))suppress) signatureN)BoundsLinearConstraintNonlinearConstraintOptimizeResult)PreparedConstraint) PRINT_OPTIONSBARRIER)CallbackSuccessget_arrays_tol)exact_1d_arraycD\rSrSrSrSrSr\S5r\S5r Sr g) ObjectiveFunctionz! Real-valued objective function. cU(aCUb[U5(de[U[5(de[U[5(deXlX lX@lSUlg)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 L/var/www/html/venv/lib/python3.13/site-packages/scipy/_lib/cobyqa/problem.py__init__ObjectiveFunction.__init__sV& ;(3-- //gt,, ,,eT** **    c[R"U[S9nURcSnU$[[R"UR"U/UR Q7655nU=R S- slUR(aB[R"S0[D6 [URSUSU35 SSS5 U$U$!,(df  U$=f)z 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__ObjectiveFunction.__call__6s HHQe $ 99 A bjj1!:tzz!:;????*eT** **XXq!f% XXa[ XXq!f% XXa[ %J&& - z}}=>Kvvk""YY :<< 3L'MN ^^ &MM+6(mmK89  66+&&YY " k\2#{l33  ^^ " {l3# |44 1&B*- "((499%&), "((499%&88DII&$)))<<88DII&YYy!|, YYy) YYy!|, YYy) 7B 7B!cchh - q"''!* -{  s 8O$OcUR$)z Left-hand side matrix of the linear inequality constraints. Returns ------- `numpy.ndarray`, shape (m, n) Left-hand side matrix of the linear inequality constraints. )rwr9s r rLinearConstraints.a_ubzzr#cUR$)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. )rxr9s r rLinearConstraints.b_ub#rr#cUR$)z Left-hand side matrix of the linear equality constraints. Returns ------- `numpy.ndarray`, shape (m, n) Left-hand side matrix of the linear equality constraints. )ryr9s r r~LinearConstraints.a_eq/rr#cUR$)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. )rzr9s r rLinearConstraints.b_eq;rr#c.URR$zl Number of linear inequality constraints. Returns ------- int Number of linear inequality constraints. )rrZr9s r m_ubLinearConstraints.m_ubGyy~~r#c.URR$zh Number of linear equality constraints. Returns ------- int Number of linear equality constraints. )rrZr9s r m_eqLinearConstraints.m_eqSrr#cJ[R"URU5SS9$)rdr'initialr+maxrfrgs r rhLinearConstraints.maxcv_svvdnnQ'55r#c[UR5(a?[R"URVs/sHo"R U5PM sn5$[R "/5$s snfN)lenr[r+rrfr,)rr2pcs r rfLinearConstraints.violationosK txx==>>TXX"FXr<<?X"FG Gxx|#GsA0)ryrwrzrxr[N)r@rCrDrErFr!rGrrr~rrrrhrfrHr*r#r rqrqsB H            6 r#rqch\rSrSrSrSrSr\S5r\S5r \S5r S S jr S S jr S r g) NonlinearConstraintsiuzA Nonlinear constraints ``c_ub(x) <= 0`` and ``c_eq(x) == b_eq``. c:U(ae[U[5(deUHn[U[5(aMe [U[5(de[U[5(deXl/UlX lSUlSUlS=Ul Ul g)z Initialize the nonlinear constraints. Parameters ---------- constraints : list Nonlinear constraints. verbose : bool Whether to print the function evaluations. debug : bool Whether to make debugging tests during the execution. N) rrurr _constraintsr[r_map_ub_map_eq_m_ub_m_eq)rrrrrs r r!NonlinearConstraints.__init__zs k400 00) !*.ABBBB*gt,, ,,eT** **'   "&& TZr#c  [UR5(d:S=UlUl[R "/5[R "/54$[R "U[ S9n[UR5(Gd/Ul/Ul SUlSUlURGHn[UR5(d2[R"U5nSUl SUl [X15nO [X!5nSURlURR#U5 [R$"URR&5nUR(SUR(Spv[+Xg5n[R,"Xv- 5U:*n URR#XY5 URR#XY)5 U=R[R."U 5- slU=R[R."U )5- slGM /n /n [1UR5GHupURRU5n UR2(aq[R4"S 0[6D6 [9[:5 URU RR<n[?USUSU 35 S S S 5 S S S 5 URU nURU nU Un[U5(aUR(SUnUR(SUnU[R@*:nUUUU- nU R#U5 U[R@:nUUUU- nU R#U5 Xn[U5(a-S UR(SUUR(SU--nUU-nU R#U5 GM UR(a[RB"U 5n O[R "/5n UR(a[RB"U 5n O[R "/5n U RDUlU RDUlX4$!,(df  GN=f!,(df  GN=f) 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%cU$rr*)x0s r /NonlinearConstraints.__call__..srr#cg)Nr'r*)rvs r rrs3r#Tr r(r)Nrtr*)#rrrrr+r,r-r[rrrjaccopyhessr r f_updatedappendarangerXr\rr{rW enumeraterr/r rrAr@r0rQrrZ)rr2rrridxrMrOarr_tolrc_ubc_eqivalfun_nameeq_idxub_idxub_valrRrT finite_xl_v finite_xueq_valmidpoints r r4NonlinearConstraints.__call__s 4$$%%&' 'DJ88B<"- - HHQe $488}}DLDLDJDJ"//  // *-A)AE.AF+A1B+J:B$( #ii)1ryy|B(0 ffRWo8  ##C$45 ##C $56 b..{;;  b.. |<< 70:txx(EA&&**Q-C}}__5}5!.1#'#4#4Q#7#;#;#D#D !A3d3%8926 \\!_F\\!_F[F6{{YYq\&)YYq\&)"&&L  ]VI%66 BK I&I6 B[F6{{"))A,v"61f9M"MN(" KK A)D ::>>$'D88B>$'D88Bs'(vv NN1wN ?  r#c[R"URVs/sHoDRU5PM sn5$s snfr)r+rr[rf)rr2rrrs r rfNonlinearConstraints.violationVs-~~B2||ABCCBsA)rrrrrrr[NN)r@rCrDrErFr!r4rGrrr:rhrfrHr*r#r rrusZ':jX**   0Dr#rcH\rSrSrSrSrSSjr\S5r\S5r \S5r \S5r \S 5r \S 5r \S 5r\S 5r\S 5r\S5r\S5r\S5r\S5r\S5r\S5r\S5rSrSSjrSSjrSrSrg)ProblemiZz Optimization problem. c U (a[U[5(de[U[5(de[U[5(de[U[5(de[U[ 5(de[U[ 5(de[U [ 5(de[U [5(deU (aU S:de[U [5(deU S:de[U [ 5(deXlX@l XPl Ub[U5(d [S5eX`l [US5nURn UR RU :wa[#SU S35eUR$R&SU :wa[#SU S 35e[)UR UR*5nUR UR*:*[,R."UR UR*- 5U:-UlS UR UR0UR*UR0--Ul[,R4"UR2UR UR0UR*UR05UlX0l[[9UR UR0)UR*UR0)55UlUR:R=X R0)5UlUR@URBSS2UR04UR2-- n[[EUR$SS2UR0)4[,RF*URHUR$SS2UR04UR2-- 5[EURBSS2UR0)4X5/URJU 5Ul U=(a UR:RL=(a [,RN"[,RP"UR:R 55=(a> [,RN"[,RP"UR:R*55nU(Ga S UR:R*UR:R - -Ul)S UR:R*UR:R --Ul*[[9[,RV"URJ5*[,RV"URJ555UlURR@URRBURT-- n[[EURR$[,RX"URR5-[,RF*URRHURR$URT-- 5[EURRB[,RX"URR5-UU5/URJU 5Ul UR>URT- URR- UlOJ[,RV"URJ5Ul)[,RZ"URJ5Ul*Xpl.Xl//Ul0/Ul1/Ul2Xl3Xl4/Ul5/Ul6/Ul7g) ai Initialize the nonlinear problem. The problem is preprocessed to remove all the variables that are fixed by the bound constraints. Parameters ---------- obj : ObjectiveFunction Objective function. x0 : array_like, shape (n,) Initial guess. bounds : BoundConstraints Bound constraints. linear : LinearConstraints Linear constraints. nonlinear : NonlinearConstraints Nonlinear constraints. callback : {callable, None} Callback function. feasibility_tol : float Tolerance on the constraint violation. scale : bool Whether to scale the problem according to the bounds. store_history : bool Whether to store the function evaluations. history_size : int Maximum number of function evaluations to store. filter_size : int Maximum number of points in the filter. debug : bool Whether to make debugging tests during the execution. rNz)The callback must be a callable function.z#The initial guess must be a vector.zThe bounds must have z elements.r z@The left-hand side matrices of the linear constraints must have z columns.rt)8rrrJrqrr-rint_obj_linear _nonlinearr TypeError _callbackrrZrRrrshaperrTr+r{ _fixed_idx _fixed_valrm _orig_boundsr_boundsrn_x0rr~rrQrrrVrUisfinite_scaling_factor_scaling_shiftrYdiagzeros_feasibility_tol _filter_size _fun_filter _maxcv_filter _x_filter_store_history _history_size _fun_history_maxcv_history _x_history)robjrr\linear nonlinearcallbackfeasibility_tolscale store_history history_size filter_sizerrtolrs r r!Problem.__init___s` c#455 55f&677 77f&788 88i)=>> >>ou55 55eT** **mT22 22lC00 00#a'''k3// //? "?eT** **  #  H%% KLL!B E F GG 99>>Q 4QCzBC C ;;  Q 1 $s)%  VYY 2!99 1 FF699vyy( )C /  IIdoo &4??)C C '' OO IIdoo & IIdoo & #' 699doo-. 4??:J0K L  <<''OO+;(<={{V[[DOO);m[>U[R"U5'[>U[R"U5'[A[CT[>5[>*5m[RD"[RF"U[>5[>*5n[RD"[RF"U[>5[>*5nTXE4$![:a n[<UeSnAff=f) at 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%rc3># UHFup[R"U5=(a TU:=(d [R"U5v MH g7frr+rS).0 fun_filter maxcv_filter maxcv_vals r #Problem.__call__..7sK 1,J$- ,*88L)*1AAc3># UHFup[R"U5=(a TU:=(d [R"U5v MH g7frr )r rrfun_vals r rrAsK 1,J&)j((88J'(1rc3J># UHupTU:=(d TU:v M g7frr*)r rrrrs r rrKs1 1,J*$@ L(@@1s #Nintermediate_result)r2r)r)$r+rer-build_xrrrhrrrrrrrpoprSrrUziprrrangerrr best_evalset parametersr StopIterationr r rminmaximumminimum)rr2penaltyx_fullrr include_pointk remove_pointsigx_bestfun_best_rexcrrs @@r r4Problem.__call__ s6 JJq &a))F#??62JJq73       $ $W -    & &y 1 OO " "1 %4$$%(:(::!!%%a(##''*##A& 88G  )!4!4 0 01Q6M XXg   14$$&&1  MXXi  14$$&&1  M  03$$&&1 M     # #G ,    % %i 0 NN ! !! $ 3t//014b"=88G$$#%88D,<,,>q,A#BL!1!1!!45?88D$6$6q$9:?"d&6&6q&99?%););A)>> !  <$$((+&&**1-NN&&q)>$4##$t'8'88  $$Q'""&&q)""1% >> %DNN+C /&*nnW&=#!f-s~~&+@*AA*8 $+' NN7JNKNN6* 88G  G%,!"%,!"c'7+gX6**RZZ9G8D**RZZ9G8D((! /%3. /sAX!'X!! X7+X22X7c.URR$)zD Number of variables. Returns ------- int Number of variables. )rrZr9s r r Problem.nsww||r#c.URR$)z Number of variables in the original problem (with fixed variables). Returns ------- int Number of variables in the original problem (with fixed variables). )rrZr9s r n_origProblem.n_origs###r#cUR$)zP Initial guess. Returns ------- `numpy.ndarray`, shape (n,) Initial guess. )rr9s r r Problem.x0r`r#c.URR$r7)rr:r9s r r:Problem.n_evalsyyr#c.URR$)r>)rr1r9s r rProblem.fun_namerr#cUR$)zM Bound constraints. Returns ------- BoundConstraints Bound constraints. )rr9s r r\Problem.boundsr<r#cUR$)zP Linear constraints. Returns ------- LinearConstraints Linear constraints. )rr9s r rProblem.linearr<r#c.URR$)zT Number of bound constraints. Returns ------- int Number of bound constraints. )r\rXr9s r m_boundsProblem.m_boundss{{}}r#c.URR$r)rrr9s r m_linear_ubProblem.m_linear_ub{{r#c.URR$r)rrr9s r m_linear_eqProblem.m_linear_eqrEr#c.URR$)z Number of nonlinear inequality constraints. Returns ------- int Number of nonlinear inequality constraints. Raises ------ ValueError If the number of nonlinear inequality constraints is not known. )rrr9s r m_nonlinear_ubProblem.m_nonlinear_ub###r#c.URR$)z Number of nonlinear equality constraints. Returns ------- int Number of nonlinear equality constraints. Raises ------ ValueError If the number of nonlinear equality constraints is not known. )rrr9s r m_nonlinear_eqProblem.m_nonlinear_eqrLr#cH[R"UR[S9$)z History of objective function evaluations. Returns ------- `numpy.ndarray`, shape (n_eval,) History of objective function evaluations. r%)r+r,rr-r9s r fun_historyProblem.fun_history(sxx))77r#cH[R"UR[S9$)z History of maximum constraint violations. Returns ------- `numpy.ndarray`, shape (n_eval,) History of maximum constraint violations. r%)r+r,rr-r9s r maxcv_historyProblem.maxcv_history4sxx++599r#cURS:dURS:agURS:dURS:agURS:agg![ a gf=f)z Type of the problem. The problem can be either 'unconstrained', 'bound-constrained', 'linearly constrained', or 'nonlinearly constrained'. Returns ------- str Type of the problem. rznonlinearly constrainedzlinearly constrainedzbound-constrained unconstrained)rJrNrCrGr@rr9s r type Problem.type@si -""Q&$*=*=*A0!!A%)9)9A)=-"*& - -  -s A AA A#"A#c URS:H$)zw Whether the problem is a feasibility problem. Returns ------- bool Whether the problem is a feasibility problem. r?)rr9s r is_feasibilityProblem.is_feasibility^s}}""r#c[R"UR5nURX R'XR -UR -X R)'URRU5$)z 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+rvr3rrrrrrn)rr2r&s r rProblem.build_xjsc$++&"&//$%(<(<$<&*&9&9%:   ((00r#NcURXUS9n[R"U5(a[R"USS9$gr)rfr+rWr)rr2rrrfs r rh Problem.maxcv~s;(NN1wNG  I & &66)S1 1r#c$/nURR(d,URRU5nURU5 [ UR R 5(a,UR RU5nURU5 [ URR 5(a-URRXU5nURU5 [ U5(a[R"U5$gr) r\rVrfrrrr[rr+r)rr2rrrfblcnlcs r rfProblem.violations {{&& %%a(A   Q  t{{  &&q)B   R t"" # #//++A@C   S ! y>>>>), , r#c[UR5S:XaU"UR5 [R"UR5n[R"UR 5n[R"UR 5n[R"U5n[R"U5(Ga7X0R:*n[R"U5(a[R"[R"X&55(dpUU[R"X&5:*-n[R"U5S:aXs[R"X75:*-n[R"U5SnGO[R"U5(a[R"U5SnGO[R "U[R"5n X%XU--X'[R"[R"U 55(a4U[R"U5:*n [R"U 5SnGOU [R"U 5:*n [R"U 5S:aX[R"X;5:*-n [R"U 5S:aX[R"X+5:*-n [R"U 5SnOp[R"[R"U55(d3U[R"U5:*n[R"U5SnO[U5S- nUR$R'XHSS245X(X84$)aV 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,rrrr|rrUrSnanminrWr" flatnonzero full_likenanr\rn) rr%rrx_filter finite_idx feasible_idx fun_min_idxr merit_filter min_maxcv_idx merit_min_idxs r rProblem.best_evals, t A % MXXd../ xx 2 23 88DNN+[[. 66*  '+@+@@Lvvl##BFF12--+"))J,D"EE ##K014266$14$KNN;/3 %%NN<04 "||J? *WJ7O-OO(66"((<011%1BIIl4K$KM}5b9A%1BIIl4K$KM'' 6:%(7:* '' 6:%rvv&58* }5b9A,-- % *(==K{+B/AJ!#A KK  A / M O  r#)rrrrrrrrrrrrrrrrrrrrr)r'r)r@rCrDrErFr!r4rGrr3rr:rr\rr@rCrGrJrNrQrTrXr[rrhrfrrHr*r#r rrZsjiVB)H   $ $                      $$ $$  8 8 : :--: # #1(4- h r#r) contextlibrinspectrrnumpyr+scipy.optimizerrrrscipy.optimize._constraintsr settingsr r utilsr rrrrJrqrrr*r#r rzso  ;-2!WWt]G]G@ddNbDbDJv  v  r#