tKgDdZddlZddlZddlmZmZddlmZddl m Z ddl m Z m Z mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$ddl%m&Z&m'Z'm(Z(d Z)d Z*d Z+ d d Z,y)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)OptimizeWarningOptimizeResult)warn)_highs_wrapper) CONST_INFMESSAGE_LEVEL_NONEHIGHS_OBJECTIVE_SENSE_MINIMIZEMODEL_STATUS_NOTSETMODEL_STATUS_LOAD_ERRORMODEL_STATUS_MODEL_ERRORMODEL_STATUS_PRESOLVE_ERRORMODEL_STATUS_SOLVE_ERRORMODEL_STATUS_POSTSOLVE_ERRORMODEL_STATUS_MODEL_EMPTYMODEL_STATUS_OPTIMALMODEL_STATUS_INFEASIBLE$MODEL_STATUS_UNBOUNDED_OR_INFEASIBLEMODEL_STATUS_UNBOUNDED5MODEL_STATUS_REACHED_DUAL_OBJECTIVE_VALUE_UPPER_BOUND%MODEL_STATUS_REACHED_OBJECTIVE_TARGETMODEL_STATUS_REACHED_TIME_LIMIT$MODEL_STATUS_REACHED_ITERATION_LIMITHIGHS_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_DEVEX0HIGHS_SIMPLEX_EDGE_WEIGHT_STRATEGY_STEEPEST_EDGE) csc_matrixvstackissparsec*iddtdtdtdtdtdt dt dtdtdtdtdtdtdtd td }d }|j||\}}|d |d |d}||fS)zCConverts HiGHS status number/message to SciPy status number/messageN)z%HiGHS did not provide a status code. )r$)r%)rz&Optimization terminated successfully. )rzTime limit reached. )rzIteration limit reached. )r&zThe problem is infeasible. )zThe problem is unbounded. )r$z(The problem is unbounded or infeasible. )r$z*The HiGHS status code was not recognized. z(HiGHS Status z: ))r r r rrrrMODEL_STATUS_RDOVUBrrrrrrrget) highs_status highs_messagescipy_statuses_messages unrecognized scipy_status scipy_messages a/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/scipy/optimize/_linprog_highs.py_highs_to_scipy_status_messager27s)C :CWC C !' C $W C !' C %gC !'C WC .wC KC ()DC -.NC !CC  AC -/B!C$EL##L,? L-%%l^2m_AGM  &&ctj|}tjd5tj||tz||<ddd|S#1swY|SxYw)Nignoreinvalid)npisinferrstatesignr)xinfss r1 _replace_infr>TsK 88A;D X &''!D'"9,$ ' H ' Hs #AA#c @ ||jS#t$r||cYSt$rptjt }|j |j}td|d|dt|jd|d td||cYSwxYw)NzOption z is z, but only values in z are allowed. Using default: .r' stacklevel) lowerAttributeErrorKeyErrorinspect signature_linprog_highs parametersdefaultrsetkeysr)option option_strchoicessig default_strs r1_convert_to_highs_enumrR\s $v||~&& v $/nnZ088  wzl$vh.CGLLN#$$A}A ,{##$sBA5BBc  | rd| d} t| tdt| dttt t dd}|\}}}}}}}}|jj\}}tjd 5tj| tjz}ddd|}|}|}tj|f}tj||f}t|s t|rt||f}ntj||f}t!|}id |d t"d |d|dt$d|d|d|d| d|d|d|dt&dt(d|d|d| } | j+| t-|}t-|}t-|}t-|}|tj.|dk(rtj0d}ntj2|}t5||j6|j8|j:|||||j=tj>| }!d|!vrH|!d}"tj2|"tA|d}#tj2|"dtA|}"nd\}"}#d|!vr|!d}$tj2|$dtA|}%tj2|$tA|d}&tj2|!dd ddf}'tj2|!ddddf}(n d\}%}&d\}'}(|!jCd!d})|!jCd"d}*tE|)|*\}+} d#|!vrtj2|!d#nd},|,|"|#tG|"|%d$tG|#|&d$tG|,dn|,|z |(d$tG|,dn||,z |'d$|!jCd%|+|!d!tHk(| |!jCd&dxs|!jCd'd|!jCd(d) }-tjJ|,rG|E|-j+|!jCd*d|!jCd+d,|!jCd-d,d.|-S#1swYxYw)/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. Currently, 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." Mathematical Programming 12.1 (1977): 361-371. zUnrecognized options detected: z). 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