o ֭jB@sdZddlZddlZddlmZmZddlmZddl m Z ddl m Z m Z mZmZmZddlmZmZmZd d Zd d Zd dZ        dddZdS)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) kHighsInfHighsDebugLevelObjSenseHighsModelStatussimplex_constants) csc_matrixvstackissparsecCsiddtjdtjdtjdtjdtjdtjdtjdtjdtj dtj dtj dtj dtj dtjd tjd }d }|||\}}|durSt|nd}|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. )rzThe problem is infeasible. )zThe problem is unbounded. )rz(The problem is unbounded or infeasible. )rz*The HiGHS status code was not recognized. z(HiGHS Status z: ))r kNotset kLoadError kModelErrorkPresolveError kSolveErrorkPostsolveError kModelEmptykObjectiveBoundkObjectiveTargetkOptimal kTimeLimitkIterationLimit kInfeasible kUnboundedkUnboundedOrInfeasiblegetint) highs_status highs_messagescipy_statuses_messages unrecognized scipy_status scipy_messagehstatr,Z/var/www/html/CropPilot/venv/lib/python3.10/site-packages/scipy/optimize/_linprog_highs.py_highs_to_scipy_status_message"sV      r.cCsRt|}tjddt||t||<Wd|S1s"wY|S)Nignoreinvalid)npisinferrstatesignr)xinfsr,r,r- _replace_inf@s  r8c Csz||WSty||YStyCtt}|j|j}td|d|dt | d|d t dd||YSw)NzOption z is z, but only values in z are allowed. Using default: .r stacklevel) lowerAttributeErrorKeyErrorinspect signature_linprog_highs parametersdefaultrsetkeysr)option option_strchoicessig default_strr,r,r-_convert_to_highs_enumHs        rKTFc .Ks| rd| d} t| tddt| dtjjtjjtjjtjjddd}|\}}}}}}}}|j \}}t j d d t | t j}Wdn1sOwY|}|}|}t ||f}t ||f}t|spt|rwt||f}nt ||f}t|}id |d tjd |d|dtjd|d|d|d| d|d|d|dtjjd|d|d| } | | t|}t|}t|}t|}|dust |dkrt d}nt |}t||j|j |j!|||||"t j#| }!d|!vr|!d}"t |"t$|d}#t |"dt$|}"nd\}"}#d|!vrU|!d}$t |$dt$|}%t |$t$|d}&t |!ddddf}'t |!ddddf}(nd\}%}&d\}'}(|!%d d})|!%d!d}*t&|)|*\}+} |!d"},|,|"|#t'|"|%d#t'|#|&d#t'|,durdn|,||(d#t'|,durdn||,|'d#|!%d$|+|!d t(j)k| |!%d%dp|!%d&d|!%d'd( }-t *|,r|dur|-|!%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. 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). These will be passed to HiGHS verbatim.rr:!simplex_dual_edge_weight_strategyN)dantzigdevexzsteepest-devexsteepestN)rHr/r0presolvesensesolver time_limithighs_debug_leveldual_feasibility_toleranceipm_optimality_tolerancelog_to_console mip_max_nodes output_flagprimal_feasibility_tolerancesimplex_strategyipm_iteration_limitsimplex_iteration_limit mip_rel_gaprslack)NNlambda marg_bndsrstatusmessager6)residual marginalsfun simplex_nitipm_nit crossover_nit) r6r_conineqlineqlinr<upperrfrbsuccessrcnitrimip_node_countmip_dual_boundgmip_gap)rprqrr)+rrrKs_cSimplexEdgeWeightStrategy!kSimplexEdgeWeightStrategyDantzigkSimplexEdgeWeightStrategyDevex kSimplexEdgeWeightStrategyChoose&kSimplexEdgeWeightStrategySteepestEdgeTcopyr2r4 ones_likeinf concatenaterr r r kMinimizerkHighsDebugLevelNoneSimplexStrategykSimplexStrategyDualupdater8sumemptyarrayrindptrindicesdataastypeuint8lenr#r.rr rany).lprRrSrPdispmaxiterrUrZrVrLr^rXunknown_optionsrc&simplex_dual_edge_weight_strategy_enumcA_ubb_ubA_eqb_eqboundsx0 integralitylbublhs_ubrhs_ublhs_eqrhs_eqlhsrhsAoptionsresr_rjlamda marg_ineqlin marg_eqlin marg_upper marg_lowerr%r&rbr6solr,r,r-rAYsF                 rA) NTFNNNNNNN)__doc__r?numpyr2 _optimizerrwarningsr_highspy._highs_wrapperr_highspy._corerrr r r rs scipy.sparser r rr.r8rKrAr,r,r,r-s(