Ë
    âQ(hzJ  ã                   ó0  — d Z ddgZddlZddlmZ ddlmZmZmZm	Z	m
Z
mZmZmZmZmZ ddlmZmZmZmZmZ dd	lmZ dd
lmZmZ ddlmZ ddlmZ dZ  e ee!«      jD                  «      Z#d„ Z$dddddddddddddde#dfd„Z%dddddddde#ddfd„Z&d„ Z'd„ Z(y)a  
This module implements the Sequential Least Squares Programming optimization
algorithm (SLSQP), originally developed by Dieter Kraft.
See http://www.netlib.org/toms/733

Functions
---------
.. autosummary::
   :toctree: generated/

    approx_jacobian
    fmin_slsqp

Úapprox_jacobianÚ
fmin_slsqpé    N)Úslsqp)
ÚzerosÚarrayÚlinalgÚappendÚconcatenateÚfinfoÚsqrtÚvstackÚisfiniteÚ
atleast_1dé   )ÚOptimizeResultÚ_check_unknown_optionsÚ_prepare_scalar_functionÚ_clip_x_for_funcÚ_check_clip_x)Úapprox_derivative)Úold_bound_to_newÚ_arr_to_scalar)Úarray_namespace)Úarray_api_extrazrestructuredtext enc                 óL   — t        || d||¬«      }t        j                  |«      S )a“  
    Approximate the Jacobian matrix of a callable function.

    Parameters
    ----------
    x : array_like
        The state vector at which to compute the Jacobian matrix.
    func : callable f(x,*args)
        The vector-valued function.
    epsilon : float
        The perturbation used to determine the partial derivatives.
    args : sequence
        Additional arguments passed to func.

    Returns
    -------
    An array of dimensions ``(lenf, lenx)`` where ``lenf`` is the length
    of the outputs of `func`, and ``lenx`` is the number of elements in
    `x`.

    Notes
    -----
    The approximation is done using forward differences.

    ú2-point)ÚmethodÚabs_stepÚargs)r   ÚnpÚ
atleast_2d)ÚxÚfuncÚepsilonr   Újacs        úV/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/scipy/optimize/_slsqp_py.pyr   r   $   s*   € ô6 ˜D !¨IÀØ!%ô'€Cô �=‰=˜ÓÐó    © éd   g�íµ ÷Æ°>c                 ó  ‡
— |�|}||||dk7  ||dœ}d}|t        ˆ
fd„|D «       «      z  }|t        ˆ
fd„|D «       «      z  }|r|d||‰
dœfz  }|r|d||	‰
dœfz  }t        | |‰
f|||d	œ|¤Ž}|r|d
   |d   |d   |d   |d   fS |d
   S )aC  
    Minimize a function using Sequential Least Squares Programming

    Python interface function for the SLSQP Optimization subroutine
    originally implemented by Dieter Kraft.

    Parameters
    ----------
    func : callable f(x,*args)
        Objective function.  Must return a scalar.
    x0 : 1-D ndarray of float
        Initial guess for the independent variable(s).
    eqcons : list, optional
        A list of functions of length n such that
        eqcons[j](x,*args) == 0.0 in a successfully optimized
        problem.
    f_eqcons : callable f(x,*args), optional
        Returns a 1-D array in which each element must equal 0.0 in a
        successfully optimized problem. If f_eqcons is specified,
        eqcons is ignored.
    ieqcons : list, optional
        A list of functions of length n such that
        ieqcons[j](x,*args) >= 0.0 in a successfully optimized
        problem.
    f_ieqcons : callable f(x,*args), optional
        Returns a 1-D ndarray in which each element must be greater or
        equal to 0.0 in a successfully optimized problem. If
        f_ieqcons is specified, ieqcons is ignored.
    bounds : list, optional
        A list of tuples specifying the lower and upper bound
        for each independent variable [(xl0, xu0),(xl1, xu1),...]
        Infinite values will be interpreted as large floating values.
    fprime : callable ``f(x,*args)``, optional
        A function that evaluates the partial derivatives of func.
    fprime_eqcons : callable ``f(x,*args)``, optional
        A function of the form ``f(x, *args)`` that returns the m by n
        array of equality constraint normals. If not provided,
        the normals will be approximated. The array returned by
        fprime_eqcons should be sized as ( len(eqcons), len(x0) ).
    fprime_ieqcons : callable ``f(x,*args)``, optional
        A function of the form ``f(x, *args)`` that returns the m by n
        array of inequality constraint normals. If not provided,
        the normals will be approximated. The array returned by
        fprime_ieqcons should be sized as ( len(ieqcons), len(x0) ).
    args : sequence, optional
        Additional arguments passed to func and fprime.
    iter : int, optional
        The maximum number of iterations.
    acc : float, optional
        Requested accuracy.
    iprint : int, optional
        The verbosity of fmin_slsqp :

        * iprint <= 0 : Silent operation
        * iprint == 1 : Print summary upon completion (default)
        * iprint >= 2 : Print status of each iterate and summary
    disp : int, optional
        Overrides the iprint interface (preferred).
    full_output : bool, optional
        If False, return only the minimizer of func (default).
        Otherwise, output final objective function and summary
        information.
    epsilon : float, optional
        The step size for finite-difference derivative estimates.
    callback : callable, optional
        Called after each iteration, as ``callback(x)``, where ``x`` is the
        current parameter vector.

    Returns
    -------
    out : ndarray of float
        The final minimizer of func.
    fx : ndarray of float, if full_output is true
        The final value of the objective function.
    its : int, if full_output is true
        The number of iterations.
    imode : int, if full_output is true
        The exit mode from the optimizer (see below).
    smode : string, if full_output is true
        Message describing the exit mode from the optimizer.

    See also
    --------
    minimize: Interface to minimization algorithms for multivariate
        functions. See the 'SLSQP' `method` in particular.

    Notes
    -----
    Exit modes are defined as follows:

    - ``-1`` : Gradient evaluation required (g & a)
    - ``0`` : Optimization terminated successfully
    - ``1`` : Function evaluation required (f & c)
    - ``2`` : More equality constraints than independent variables
    - ``3`` : More than 3*n iterations in LSQ subproblem
    - ``4`` : Inequality constraints incompatible
    - ``5`` : Singular matrix E in LSQ subproblem
    - ``6`` : Singular matrix C in LSQ subproblem
    - ``7`` : Rank-deficient equality constraint subproblem HFTI
    - ``8`` : Positive directional derivative for linesearch
    - ``9`` : Iteration limit reached

    Examples
    --------
    Examples are given :ref:`in the tutorial <tutorial-sqlsp>`.

    r   )ÚmaxiterÚftolÚiprintÚdispÚepsÚcallbackr(   c              3   ó*   •K  — | ]
  }d |‰dœ–— Œ y­w)Úeq©ÚtypeÚfunr   Nr(   ©Ú.0Úcr   s     €r&   ú	<genexpr>zfmin_slsqp.<locals>.<genexpr>Ä   s   øè ø€ ÒI¸Q˜4¨°4Õ8ÑIùó   ƒc              3   ó*   •K  — | ]
  }d |‰dœ–— Œ y­w)Úineqr3   Nr(   r6   s     €r&   r9   zfmin_slsqp.<locals>.<genexpr>Å   s   øè ø€ ÒL¸q˜6¨!°TÕ:ÑLùr:   r2   )r4   r5   r%   r   r<   )r%   ÚboundsÚconstraintsr"   r5   ÚnitÚstatusÚmessage)ÚtupleÚ_minimize_slsqp)r#   Úx0ÚeqconsÚf_eqconsÚieqconsÚ	f_ieqconsr=   ÚfprimeÚfprime_eqconsÚfprime_ieqconsr   ÚiterÚaccr-   r.   Úfull_outputr$   r0   ÚoptsÚconsÚress             `          r&   r   r   F   sú   ø€ ð` ÐØˆàØØØ˜a‘KØØ ñ"€Dð €Dð 	ŒEÓIÀ&ÔIÓIÑI€DØŒEÓLÀGÔLÓLÑL€Dñ Ø˜$ x¸Øñ ð #ñ 	#ˆáØ˜&¨¸>Øñ ð #ñ 	#ˆô ˜$  Dð 4¨f¸VØ&*ñ4Ø.2ñ4€CáØ�3‰x˜˜U™ S¨¡Z°°X±ÀÀIÁÐNÐNà�3‰xˆr'   Fc                 óþ  ‡‡‡H‡I— t        |«       |dz
  }|}|
ŠH|	sd}t        |«      }t        j                  |j	                  |«      d|¬«      }|j
                  }|j                  |j                  d«      r|j                  }|j                  |j                  ||«      d«      }|�t        |«      dk(  r"t        j                   t        j                  fŠInt        |«      ŠIt        j                  |‰Id   ‰Id   «      }t        |t         «      r|f}dddœ}t#        |«      D ]Œ  \  }}	 |d	   j%                  «       }|dvrt'        d
|d	   › d�«      ‚d|vrt'        d|z  «      ‚|j/                  d«      }|€ˆHˆˆˆIfd„} ||d   «      }||xx   |d   ||j/                  dd«      dœfz  cc<   ŒŽ dddddddddddd œ}t1        t3        t        |d!   D �cg c]  }t5         |d   |g|d   ¢­Ž «      ‘Œ c}«      «      }t1        t3        t        |d"   D �cg c]  }t5         |d   |g|d   ¢­Ž «      ‘Œ c}«      «      }||z   }t7        d|g«      j9                  «       }t        |«      } | dz   }!||z
  |!z   |!z   }"d#|!z  |z   |!dz   z  |!|z
  dz   |"d$z   z  z   d$|"z  z   |!|"z   |!|z
  z  z   d$|z  z   |!z   | dz   | z  d$z  z   d$|z  z   d#| z  z   d#|!z  z   dz   }#|"}$t;        |#«      }%t;        |$«      }&|�t        |«      dk(  rvt        j<                  | t>        ¬%«      }'t        j<                  | t>        ¬%«      }(|'jA                  t        jB                  «       |(jA                  t        jB                  «       �nt7        |D �)�*cg c]  \  })}*tE        |)«      tE        |*«      f‘Œ c}*})t>        «      }+|+jF                  d   | k7  rtI        d&«      ‚t        jJ                  d'¬(«      5  |+dd…df   |+dd…df   kD  },ddd«       ,jM                  «       r%t'        d)d*jO                  d+„ |,D «       «      › d,�«      ‚|+dd…df   |+dd…df   }(}'tQ        |+«       }-t        jB                  |'|-dd…df   <   t        jB                  |(|-dd…df   <   tS        | |‰||
‰‰I¬-«      }.tU        |.jV                  ‰I«      }/tU        |.jX                  ‰I«      }0t7        dtZ        «      }1t7        |t>        «      }t7        |tZ        «      }2d}3t7        dt>        «      }4t7        dt>        «      }5t7        dt>        «      }6t7        dt>        «      }7t7        dt>        «      }8t7        dt>        «      }9t7        dt>        «      }:t7        dt>        «      };t7        dt>        «      }<t7        dt>        «      }=t7        dtZ        «      }>t7        dtZ        «      }?t7        dtZ        «      }@t7        dtZ        «      }At7        dtZ        «      }Bt7        dtZ        «      }!t7        dtZ        «      }Ct7        dtZ        «      }D|d$k\  rt]        d.d/z  «        |/|«      }Et_         |0|«      d0«      }Fta        ||«      }tc        |||| |||«      }G	 te        g |‘|‘|‘|'‘|(‘E‘|‘F‘G‘|‘|2‘|1‘|%‘|&‘|4‘|5‘|6‘|7‘|8‘|9‘|:‘|;‘|<‘|=‘|>‘|?‘@‘A‘B‘|!‘C‘D‘­Ž  |1dk(  r |/|«      }Eta        ||«      }|1dk(  r#t_         |0|«      d0«      }Ftc        |||| |||«      }G|2|3kD  rQ|� |t        jf                  |«      «       |d$k\  r/t]        d1|2|.jh                  Etk        jl                  F«      fz  «       to        |1«      dk7  rnt[        |2«      }3Œû|dk\  rmt]        |t[        |1«         d2z   tq        |1«      z   d3z   «       t]        d4E«       t]        d5|2«       t]        d6|.jh                  «       t]        d7|.jr                  «       tu        |EFdd t[        |2«      |.jh                  |.jr                  t[        |1«      |t[        |1«         |1dk(  ¬8«	      S # t(        $ r}t)        d|z  «      |‚d}~wt*        $ r}t+        d«      |‚d}~wt,        $ r}t+        d«      |‚d}~ww xY wc c}w c c}w c c}*})w # 1 sw Y   �Œ…xY w)9aª  
    Minimize a scalar function of one or more variables using Sequential
    Least Squares Programming (SLSQP).

    Options
    -------
    ftol : float
        Precision goal for the value of f in the stopping criterion.
    eps : float
        Step size used for numerical approximation of the Jacobian.
    disp : bool
        Set to True to print convergence messages. If False,
        `verbosity` is ignored and set to 0.
    maxiter : int
        Maximum number of iterations.
    finite_diff_rel_step : None or array_like, optional
        If ``jac in ['2-point', '3-point', 'cs']`` the relative step size to
        use for numerical approximation of `jac`. The absolute step
        size is computed as ``h = rel_step * sign(x) * max(1, abs(x))``,
        possibly adjusted to fit into the bounds. For ``method='3-point'``
        the sign of `h` is ignored. If None (default) then step is selected
        automatically.
    r   r   )ÚndimÚxpzreal floatingéÿÿÿÿNr(   )r2   r<   r4   zUnknown constraint type 'z'.z"Constraint %d has no type defined.z/Constraints must be defined using a dictionary.z#Constraint's type must be a string.r5   z&Constraint %d has no function defined.r%   c                 ó   •‡ — ˆˆˆ ˆˆfd„}|S )Nc                 óh   •— t        | ‰«      } ‰dv rt        ‰| ‰|‰‰¬«      S t        ‰| d‰|‰¬«      S )N)r   z3-pointÚcs)r   r   Úrel_stepr=   r   )r   r   r   r=   )r   r   )r"   r   r$   Úfinite_diff_rel_stepr5   r%   Ú
new_boundss     €€€€€r&   Úcjacz3_minimize_slsqp.<locals>.cjac_factory.<locals>.cjac,  sT   ø€ Ü% a¨Ó4�AàÐ:Ñ:Ü0°°aÀÈ$Ø:NØ8Bô Dð Dô  1°°aÀ	Ø:AÈØ8Bô Dð Dr'   r(   )r5   r\   r$   rZ   r%   r[   s   ` €€€€r&   Úcjac_factoryz%_minimize_slsqp.<locals>.cjac_factory+  s   ù€ ÷
Dð 
Dð �r'   r   )r5   r%   r   z$Gradient evaluation required (g & a)z$Optimization terminated successfullyz$Function evaluation required (f & c)z4More equality constraints than independent variablesz*More than 3*n iterations in LSQ subproblemz#Inequality constraints incompatiblez#Singular matrix E in LSQ subproblemz#Singular matrix C in LSQ subproblemz2Rank-deficient equality constraint subproblem HFTIz.Positive directional derivative for linesearchzIteration limit reached)rU   r   r   é   é   é   é   é   é   é   é	   r2   r<   r_   r^   )ÚdtypezDSLSQP Error: the length of bounds is not compatible with that of x0.Úignore)ÚinvalidzSLSQP Error: lb > ub in bounds z, c              3   ó2   K  — | ]  }t        |«      –— Œ y ­w)N)Ústr)r7   Úbs     r&   r9   z"_minimize_slsqp.<locals>.<genexpr>t  s   è ø€ Ò)A°Q¬#¨a¯&Ñ)Aùs   ‚ú.)r%   r   r$   rZ   r=   z%5s %5s %16s %16s)ÚNITÚFCÚOBJFUNÚGNORMg        z%5i %5i % 16.6E % 16.6Ez    (Exit mode ú)z#            Current function value:z            Iterations:z!            Function evaluations:z!            Gradient evaluations:)	r"   r5   r%   r?   ÚnfevÚnjevr@   rA   Úsuccess);r   r   ÚxpxÚ
atleast_ndÚasarrayÚfloat64Úisdtyperf   ÚreshapeÚastypeÚlenr    Úinfr   ÚclipÚ
isinstanceÚdictÚ	enumerateÚlowerÚ
ValueErrorÚKeyErrorÚ	TypeErrorÚAttributeErrorÚgetÚsumÚmapr   r   Úmaxr   ÚemptyÚfloatÚfillÚnanr   ÚshapeÚ
IndexErrorÚerrstateÚanyÚjoinr   r   r   r5   ÚgradÚintÚprintr	   Ú_eval_constraintÚ_eval_con_normalsr   Úcopyrr   r   ÚnormÚabsrj   Úngevr   )Jr#   rD   r   r%   r=   r>   r+   r,   r-   r.   r/   r0   rZ   Úunknown_optionsrL   rM   rT   rf   r"   rP   ÚicÚconÚctypeÚer\   r]   Ú
exit_modesr8   ÚmeqÚmieqÚmÚlaÚnÚn1ÚmineqÚlen_wÚlen_jwÚwÚjwÚxlÚxuÚlÚuÚbndsÚbnderrÚinfbndÚsfÚwrapped_funÚwrapped_gradÚmodeÚmajiterÚmajiter_prevÚalphaÚf0ÚgsÚh1Úh2Úh3Úh4ÚtÚt0ÚtolÚiexactÚinconsÚiresetÚitermxÚlineÚn2Ún3ÚfxÚgÚar$   r[   sJ      `        `                                                           @@r&   rC   rC   Ø   s<	  û€ ô8 ˜?Ô+Ø�Q‰;€DØ
€CØ€GáØˆô 
˜Ó	€BÜ	�‰˜Ÿ
™
 2›¨Q°2Ô	6€BØ�J‰J€EØ	‡z�z�"—(‘(˜OÔ,Ø—‘ˆØ
�
‰
�2—9‘9˜R Ó'¨Ó,€Að €~œ˜V›¨Ò)Ü—v‘v�gœrŸv™vÐ&‰
ä% fÓ-ˆ
ô 	�‰��:˜a‘= *¨Q¡-Ó0€Aô �+œtÔ$Ø"�oˆà˜bÑ!€DÜ˜[Ó)ò +9‰ˆˆCð	NØ˜‘K×%Ñ%Ó'ˆEð ˜NÑ*Ü Ð#<¸SÀ¹[¸MÈÐ!LÓMÐMð ˜ÑÜÐEÈÑJÓKÐKð �w‰w�u‹~ˆØˆ<÷ñ    E¡
Ó+ˆDð 	ˆU‹  E¡
Ø $Ø!$§¡¨°Ó!4ñ6ð 9ñ 	9ŒðS+9ðZ =Ø<Ø<ØLØBØ;Ø;Ø;ØJØFØ/ñ
1€Jô Œc”#Ø˜D‘zö#Øô # 8 1 U¡8¨AÐ#:°°&±	Ò#:Õ;ò #ó $ó %€CäŒs”3Ø˜V™ö&Øô $ H A e¡H¨QÐ$;°°6±Ò$;Õ<ò &ó 'ó (€Dð 	ˆd‰
€Aä	��1ˆv‹×	Ñ	Ó	€BäˆA‹€Að 
ˆQ‰€BØ�‰G�b‰L˜2Ñ€EØˆr‰T�!‰V�b˜‘d‰O˜R ™V A™X¨¨a©Ñ0Ñ0°1°U±7Ñ:¸B¸u¹HÀrÈ#ÁvÑ;NÑNØ�‰eñØñØ˜q™S !™G a™<ñ(Ø*+¨A©#ñ.Ø01°!±ñ4Ø67¸±dñ;Ø=>ñ?€Eà€FÜˆe‹€AÜ	ˆv‹€Bð €~œ˜V›¨Ò)Ü�X‰X�aœuÔ%ˆÜ�X‰X�aœuÔ%ˆØ
�‰”—‘ŒØ
�‰”—‘ŽäØ$*÷,Ù ˜1˜aô & aÓ(¬.¸Ó*;Ò<ó ,Ü-2ó4ˆà�:‰:�a‰=˜AÒÜð ;ó <ð <ô �[‰[ Ô*ñ 	-Øš!˜Q˜$‘Z $¢q¨! t¡*Ñ,ˆF÷	-ð �:‰:Œ<ÜÐ>Ø $§	¡	Ñ)A¸&Ô)AÓ AÐBÀ!ðEó Fð Fà’a˜�d‘˜T¢! Q $™ZˆBˆô ˜4“.�ˆÜŸ6™6ˆˆ6’!�Q�$‰<ÑÜŸ6™6ˆˆ6’!�Q�$‰<Ñô 
" $¨¨s¸ÀsØ7KØ)3ô
5€Bô
 # 2§6¡6¨:Ó6€KÜ# B§G¡G¨ZÓ8€Lô �”C‹=€DÜ
�”UÓ
€CÜ�Dœ#Ó€GØ€Lô �!”U‹O€EÜ	ˆq”%‹€BÜ	ˆq”%‹€BÜ	ˆq”%‹€BÜ	ˆq”%‹€BÜ	ˆq”%‹€BÜ	ˆq”%‹€BÜˆa”‹€AÜ	ˆq”%‹€BÜ
�”5‹/€CÜ�1”c‹]€FÜ�1”c‹]€FÜ�1”c‹]€FÜ�1”c‹]€FÜ�”C‹=€DÜ	ˆq”#‹€BÜ	ˆq”#‹€BÜ	ˆq”#‹€Bð �‚{ÜÐ!Ð$DÑDÔEñ
 
�Q‹€BÜ‰|˜A‹ Ó$€AÜ˜˜DÓ!€AÜ˜!˜T 2 q¨!¨S°$Ó7€Aà
äð 	ˆað 	�ð 	�að 	˜ð 	˜Rð 	 ð 	 Qð 	¨ð 	¨1ð 	¨cð 	°7ð 	¸Dð 	À!ð 	ÀRð 	Øð	Øð	Øð	Øð	Ø!#ð	Ø%'ð	Ø)+ð	Ø-.ð	Ø02ð	Ø47ð	àð	àð	à$ð	à&,ð	à.2ð	ð ð	ð ð	ð ó	ð
 �1Š9Ù˜Q“ˆBÜ   DÓ)ˆAà�2Š:Ü‘| A“¨Ó,ˆAÜ! ! T¨2¨q°!°S¸$Ó?ˆAà�\Ò!àÐ#ÙœŸ™ ›Ô$ð ˜Š{ÜÐ/°7¸B¿G¹GØ35´v·{±{À1³~ð3Gñ Gô Hô ˆt‹9˜Š>Øä˜7“|ˆð; ð@ �‚{Üˆjœ˜T›Ñ#Ð&7Ñ7¼#¸d»)ÑCÀcÑIÔJÜÐ3°RÔ8ÜÐ'¨Ô1ÜÐ1°2·7±7Ô;ÜÐ1°2·7±7Ô;ä˜A 2¨1¨S¨b¨6´s¸7³|Ø!Ÿw™w¨R¯W©W¼SÀ»YØ",¬S°«YÑ"7À$È!Á)ôNð Nøôw ò 	MÜÐ?À"ÑDÓEÈ1ÐLûÜò 	2Üð *ó +Ø01ð2ûäò 	JÜÐAÓBÈÐIûð	Jüòd#ùò&ùó2,÷	-ñ 	-úsN   Ä)`Ça"Èa'Í; a,
Ïa2à	aà`*à*aà6aáaáaáaá2a<c                 ó@  — |d   r3t        |d   D �cg c]  }t         |d   | g|d   ¢­Ž «      ‘Œ c}«      }nt        d«      }|d   r3t        |d   D �cg c]  }t         |d   | g|d   ¢­Ž «      ‘Œ c}«      }nt        d«      }t        ||f«      }|S c c}w c c}w )Nr2   r5   r   r   r<   )r
   r   r   )r"   rP   rŸ   Úc_eqÚc_ieqr8   s         r&   r—   r—   ×  s¿   € àˆD‚zÜØ'+¨D¡zö3Ø #ô ' z s¨5¡z°!Ð'B°c¸&±kÒ'BÕCò 3ó 4‰ô �Q‹xˆàˆF‚|ÜØ(,¨V©ö6Ø!$ô (¨
¨¨E©
°1Ð(C°s¸6±{Ò(CÕDò 6ó 7‰ô �a“ˆô 	�T˜5�MÓ"€AØ€Hùò3ùò6s   ’BÁBc           
      ó|  — |d   r*t        |d   D �cg c]  } |d   | g|d   ¢­Ž ‘Œ c}«      }nt        ||f«      }|d   r*t        |d   D �cg c]  } |d   | g|d   ¢­Ž ‘Œ c}«      }	nt        ||f«      }	|dk(  rt        ||f«      }
nt        ||	f«      }
t        |
t        |dg«      fd«      }
|
S c c}w c c}w )Nr2   r%   r   r<   r   r   )r   r   r
   )r"   rP   r¦   r§   r¥   r£   r¤   rŸ   Úa_eqÚa_ieqrÎ   s              r&   r˜   r˜   ê  së   € àˆD‚zÜØ"& t¡*ö.Øð "�s˜5‘z !Ð2 c¨&¡kÔ2ò .ó /‰ô �c˜1�X‹ˆàˆF‚|ÜØ#'¨¡<ö1Øð #˜˜E™
 1Ð3 s¨6¡{Ô3ò 1ó 2‰ô �t˜Q�iÓ ˆð 	ˆA‚vÜ�2�q�'‹N‰ä�D˜%�=Ó!ˆÜ�Qœ˜r 1˜g›Ð'¨Ó+€Aà€Hùò%.ùò1s   ’B4ÁB9))Ú__doc__Ú__all__Únumpyr    Úscipy.optimize._slsqpr   r   r   r   r	   r
   r   r   r   r   r   Ú	_optimizer   r   r   r   r   Ú_numdiffr   Ú_constraintsr   r   Úscipy._lib._array_apir   Ú
scipy._libr   ru   Ú__docformat__rŒ   r/   Ú_epsilonr   r   rC   r—   r˜   r(   r'   r&   ú<module>rà      sÀ   ðñð ˜lÐ
+€ã Ý '÷7÷ 7÷ 7÷'õ 'õ (ß :Ý 1Ý -ð &€á‘�e“× Ñ Ó!€òðD !#¨T¸2ÈØ °TØ"¨°#¸6Ø˜d°¸8Øó	Oðd $&¨4¸Ø "Ø f°Q¸UØ ¨4Àdó|Nò~ó&r'   