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Editing: _linprog_highs.cpython-311.pyc
� d�c8D � � � d Z ddlZddlZddlmZmZmZ ddlm Z ddl m Z ddl m Z mZmZmZmZmZmZmZmZmZmZmZmZmZmZm Z m Z m Z m Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z( ddl)m*Z*m+Z+m,Z, d � Z-d � Z.d � Z/ dd�Z0dS )a^ HiGHS Linear Optimization Methods Interface to HiGHS linear optimization software. https://highs.dev/ .. versionadded:: 1.5.0 References ---------- .. [1] Q. Huangfu and J.A.J. Hall. "Parallelizing the dual revised simplex method." Mathematical Programming Computation, 10 (1), 119-142, 2018. DOI: 10.1007/s12532-017-0130-5 � N� )�_check_unknown_options�OptimizeWarning�OptimizeResult)�warn)�_highs_wrapper)� CONST_I_INF� CONST_INF�MESSAGE_LEVEL_NONE� HIGHS_OBJECTIVE_SENSE_MINIMIZE�MODEL_STATUS_NOTSET�MODEL_STATUS_LOAD_ERROR�MODEL_STATUS_MODEL_ERROR�MODEL_STATUS_PRESOLVE_ERROR�MODEL_STATUS_SOLVE_ERROR� MODEL_STATUS_POSTSOLVE_ERROR�MODEL_STATUS_MODEL_EMPTY�MODEL_STATUS_OPTIMAL�MODEL_STATUS_INFEASIBLE�$MODEL_STATUS_UNBOUNDED_OR_INFEASIBLE�MODEL_STATUS_UNBOUNDED�5MODEL_STATUS_REACHED_DUAL_OBJECTIVE_VALUE_UPPER_BOUND�%MODEL_STATUS_REACHED_OBJECTIVE_TARGET� MODEL_STATUS_REACHED_TIME_LIMIT�$MODEL_STATUS_REACHED_ITERATION_LIMIT� HIGHS_SIMPLEX_STRATEGY_CHOOSE�HIGHS_SIMPLEX_STRATEGY_DUAL� HIGHS_SIMPLEX_CRASH_STRATEGY_OFF�)HIGHS_SIMPLEX_EDGE_WEIGHT_STRATEGY_CHOOSE�*HIGHS_SIMPLEX_EDGE_WEIGHT_STRATEGY_DANTZIG�(HIGHS_SIMPLEX_EDGE_WEIGHT_STRATEGY_DEVEX�0HIGHS_SIMPLEX_EDGE_WEIGHT_STRATEGY_STEEPEST_EDGE�HIGHS_VAR_TYPE_CONTINUOUS)� csc_matrix�vstack�issparsec �P � i dd�t d�t d�t d�t d�t d�t d�t d�t d�t d�t d�t d�t d�t d�t d �t d �}d }|� | |� � \ }}|� d | � d |� d�}||fS )zCConverts HiGHS status number/message to SciPy status number/messageN)� z%HiGHS did not provide a status code. )r( � )� r) )r z&Optimization terminated successfully. )r zTime limit reached. )r zIteration limit reached. )r* zThe problem is infeasible. )� zThe problem is unbounded. )r( z(The problem is unbounded or infeasible. )r( z*The HiGHS status code was not recognized. z(HiGHS Status z: �))r r r r r r r �MODEL_STATUS_RDOVUBr r r r r r r �get)� highs_status� highs_message�scipy_statuses_messages� unrecognized� scipy_status� scipy_messages �?/usr/lib/python3/dist-packages/scipy/optimize/_linprog_highs.py� _highs_to_scipy_status_messager6 ; sE � � C� �:� C��W� C� �� C� !�'� C� $�W� C� !�'� C� %�g� C� !�'� C� �W� C� .�w� C� � K� C� (�)D� C� -�.N� C� �!C� C� � A� C� -� /B�! 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Using default: �.r+ )� stacklevel) �lower�AttributeError�KeyError�inspect� signature�_linprog_highs� parameters�defaultr �set�keysr )�option� option_str�choices�sig� default_strs r5 �_convert_to_highs_enumrQ _ s� � � $��v�|�|�~�~�&�&�� � � � ��v�� � � � � $� $� $�� ��/�/���n�Z�0�8� � � �z� � �v� � ��G�L�L�N�N�#�#� � �� � � � �� ,� ,� ,� ,� �{�#�#�#�#�$���s � �B2�A?B2�1B2TFc �x � t | � � t | dt t t t dd��� � } | \ }}}}}}}}|j � � � \ }}t j |� � t j z }|}|}|}t j ||f� � } t j ||f� � } t |� � st |� � rt ||f� � } nt j ||f� � } t | � � } i d|�dt �d|�d|�d t �d |�d |�d |�d | �d|�d|�d| �dt" �dt$ �d|�d|�d| �} t' | � � } t' | � � } t' |� � }t' |� � }|�t j |� � dk rt j d� � }nt j |� � }t/ || j | j | j | | |||� t j � � | � � } d| v r[| d }!t j |!t; |� � d� � � }"t j |!dt; |� � � � � }!nd\ }!}"d| v r�| d }#t j |#dt; |� � � � � }$t j |#t; |� � d� � � }%t j | d ddd�f � � }&t j | d ddd�f � � }'n d\ }$}%d\ }&}'dd d d �}(| � d d� � })| � d d� � }*t? |)|*� � \ }+},d | v rt j | d � � nd}-|-|!|"tA |!|$d!�� � tA |"|%d!�� � tA |-�dn|-|z |'d!�� � tA |-�dn||-z |&d!�� � | � d"� � |+| d tB k |,| � d#d� � p| � d$d� � | � d%� � d&� }.t j" |-� � rW|�U|.�# | � d'd� � | � d(d)� � | � d*d)� � d+�� � |.S ),a� Solve the following linear programming problem using one of the HiGHS solvers: User-facing documentation is in _linprog_doc.py. Parameters ---------- lp : _LPProblem A ``scipy.optimize._linprog_util._LPProblem`` ``namedtuple``. solver : "ipm" or "simplex" or None Which HiGHS solver to use. If ``None``, "simplex" will be used. Options ------- maxiter : int The maximum number of iterations to perform in either phase. For ``solver='ipm'``, this does not include the number of crossover iterations. Default is the largest possible value for an ``int`` on the platform. disp : bool Set to ``True`` if indicators of optimization status are to be printed to the console each iteration; default ``False``. time_limit : float The maximum time in seconds allotted to solve the problem; default is the largest possible value for a ``double`` on the platform. presolve : bool Presolve attempts to identify trivial infeasibilities, identify trivial unboundedness, and simplify the problem before sending it to the main solver. It is generally recommended to keep the default setting ``True``; set to ``False`` if presolve is to be disabled. dual_feasibility_tolerance : double Dual feasibility tolerance. Default is 1e-07. The minimum of this and ``primal_feasibility_tolerance`` is used for the feasibility tolerance when ``solver='ipm'``. primal_feasibility_tolerance : double Primal feasibility tolerance. Default is 1e-07. The minimum of this and ``dual_feasibility_tolerance`` is used for the feasibility tolerance when ``solver='ipm'``. ipm_optimality_tolerance : double Optimality tolerance for ``solver='ipm'``. Default is 1e-08. Minimum possible value is 1e-12 and must be smaller than the largest possible value for a ``double`` on the platform. simplex_dual_edge_weight_strategy : str (default: None) Strategy for simplex dual edge weights. The default, ``None``, automatically selects one of the following. ``'dantzig'`` uses Dantzig's original strategy of choosing the most negative reduced cost. ``'devex'`` uses the strategy described in [15]_. ``steepest`` uses the exact steepest edge strategy as described in [16]_. ``'steepest-devex'`` begins with the exact steepest edge strategy until the computation is too costly or inexact and then switches to the devex method. Curently, using ``None`` always selects ``'steepest-devex'``, but this may change as new options become available. mip_max_nodes : int The maximum number of nodes allotted to solve the problem; default is the largest possible value for a ``HighsInt`` on the platform. Ignored if not using the MIP solver. unknown_options : dict Optional arguments not used by this particular solver. If ``unknown_options`` is non-empty, a warning is issued listing all unused options. Returns ------- sol : dict A dictionary consisting of the fields: x : 1D array The values of the decision variables that minimizes the objective function while satisfying the constraints. fun : float The optimal value of the objective function ``c @ x``. slack : 1D array The (nominally positive) values of the slack, ``b_ub - A_ub @ x``. con : 1D array The (nominally zero) residuals of the equality constraints, ``b_eq - A_eq @ x``. success : bool ``True`` when the algorithm succeeds in finding an optimal solution. status : int An integer representing the exit status of the algorithm. ``0`` : Optimization terminated successfully. ``1`` : Iteration or time limit reached. ``2`` : Problem appears to be infeasible. ``3`` : Problem appears to be unbounded. ``4`` : The HiGHS solver ran into a problem. message : str A string descriptor of the exit status of the algorithm. nit : int The total number of iterations performed. For ``solver='simplex'``, this includes iterations in all phases. For ``solver='ipm'``, this does not include crossover iterations. crossover_nit : int The number of primal/dual pushes performed during the crossover routine for ``solver='ipm'``. This is ``0`` for ``solver='simplex'``. ineqlin : OptimizeResult Solution and sensitivity information corresponding to the inequality constraints, `b_ub`. A dictionary consisting of the fields: residual : np.ndnarray The (nominally positive) values of the slack variables, ``b_ub - A_ub @ x``. This quantity is also commonly referred to as "slack". marginals : np.ndarray The sensitivity (partial derivative) of the objective function with respect to the right-hand side of the inequality constraints, `b_ub`. eqlin : OptimizeResult Solution and sensitivity information corresponding to the equality constraints, `b_eq`. A dictionary consisting of the fields: residual : np.ndarray The (nominally zero) residuals of the equality constraints, ``b_eq - A_eq @ x``. marginals : np.ndarray The sensitivity (partial derivative) of the objective function with respect to the right-hand side of the equality constraints, `b_eq`. lower, upper : OptimizeResult Solution and sensitivity information corresponding to the lower and upper bounds on decision variables, `bounds`. residual : np.ndarray The (nominally positive) values of the quantity ``x - lb`` (lower) or ``ub - x`` (upper). marginals : np.ndarray The sensitivity (partial derivative) of the objective function with respect to the lower and upper `bounds`. mip_node_count : int The number of subproblems or "nodes" solved by the MILP solver. Only present when `integrality` is not `None`. mip_dual_bound : float The MILP solver's final estimate of the lower bound on the optimal solution. Only present when `integrality` is not `None`. mip_gap : float The difference between the final objective function value and the final dual bound, scaled by the final objective function value. Only present when `integrality` is not `None`. Notes ----- The result fields `ineqlin`, `eqlin`, `lower`, and `upper` all contain `marginals`, or partial derivatives of the objective function with respect to the right-hand side of each constraint. These partial derivatives are also referred to as "Lagrange multipliers", "dual values", and "shadow prices". The sign convention of `marginals` is opposite that of Lagrange multipliers produced by many nonlinear solvers. References ---------- .. [15] Harris, Paula MJ. "Pivot selection methods of the Devex LP code." Mathematical programming 5.1 (1973): 1-28. .. [16] Goldfarb, Donald, and John Ker Reid. "A practicable steepest-edge simplex algorithm." 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