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# linprog(method=’highs-ds’)

scipy.optimize.linprog(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None, bounds=(0, None), method='highs', callback=None, options=None, x0=None, integrality=None)

  Linear programming: minimize a linear objective function subject to linear equality and inequality constraints using the HiGHS dual simplex solver.

  Linear programming solves problems of the following form:

  \[\begin{split}\min_x \ & c^T x \\ \mbox{such that} \ & A_{ub} x \leq b_{ub},\\ & A_{eq} x = b_{eq},\\ & l \leq x \leq u ,\end{split}\]

  where \(x\) is a vector of decision variables; \(c\), \(b_{ub}\), \(b_{eq}\), \(l\), and \(u\) are vectors; and \(A_{ub}\) and \(A_{eq}\) are matrices.

  Alternatively, that’s:

  minimize:

      c @ x

  such that:

      A_ub @ x <= b_ub
      A_eq @ x == b_eq
      lb <= x <= ub

  Note that by default `lb = 0` and `ub = None` unless specified with `bounds`.

  Parameters:
    c 1-D array

      The coefficients of the linear objective function to be minimized.

    A_ub 2-D array, optional

      The inequality constraint matrix. Each row of `A_ub` specifies the coefficients of a linear inequality constraint on `x`.

    b_ub 1-D array, optional

      The inequality constraint vector. Each element represents an upper bound on the corresponding value of `A_ub @ x`.

    A_eq 2-D array, optional

      The equality constraint matrix. Each row of `A_eq` specifies the coefficients of a linear equality constraint on `x`.

    b_eq 1-D array, optional

      The equality constraint vector. Each element of `A_eq @ x` must equal the corresponding element of `b_eq`.

    bounds sequence, optional

      A sequence of `(min, max)` pairs for each element in `x`, defining the minimum and maximum values of that decision variable. Use `None` to indicate that there is no bound. By default, bounds are `(0, None)` (all decision variables are non-negative). If a single tuple `(min, max)` is provided, then `min` and `max` will serve as bounds for all decision variables.

    method str

      This is the method-specific documentation for ‘highs-ds’. ‘highs’, ‘highs-ipm’, ‘interior-point’ (default), ‘revised simplex’, and ‘simplex’ (legacy) are also available.

  Returns:
    res OptimizeResult

      A `scipy.optimize.OptimizeResult` consisting of the fields:

      x1D array

        The values of the decision variables that minimizes the objective function while satisfying the constraints.

      funfloat

        The optimal value of the objective function `c @ x`.

      slack1D array

        The (nominally positive) values of the slack, `b_ub - A_ub @ x`.

      con1D array

        The (nominally zero) residuals of the equality constraints, `b_eq - A_eq @ x`.

      successbool

        `True` when the algorithm succeeds in finding an optimal solution.

      statusint

        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.

      messagestr

        A string descriptor of the exit status of the algorithm.

      nitint

        The total number of iterations performed. This includes iterations in all phases.

      crossover_nitint

        This is always `0` for the HiGHS simplex method. For the HiGHS interior-point method, this is the number of primal/dual pushes performed during the crossover routine.

      ineqlinOptimizeResult

        Solution and sensitivity information corresponding to the inequality constraints, b_ub. A dictionary consisting of the fields:

        residualnp.ndnarray

          The (nominally positive) values of the slack variables, `b_ub - A_ub @ x`. This quantity is also commonly referred to as “slack”.

        marginalsnp.ndarray

          The sensitivity (partial derivative) of the objective function with respect to the right-hand side of the inequality constraints, b_ub.

      eqlinOptimizeResult

        Solution and sensitivity information corresponding to the equality constraints, b_eq. A dictionary consisting of the fields:

        residualnp.ndarray

          The (nominally zero) residuals of the equality constraints, `b_eq - A_eq @ x`.

        marginalsnp.ndarray

          The sensitivity (partial derivative) of the objective function with respect to the right-hand side of the equality constraints, b_eq.

      lower, upperOptimizeResult

        Solution and sensitivity information corresponding to the lower and upper bounds on decision variables, bounds.

        residualnp.ndarray

          The (nominally positive) values of the quantity `x - lb` (lower) or `ub - x` (upper).

        marginalsnp.ndarray

          The sensitivity (partial derivative) of the objective function with respect to the lower and upper bounds.

  See also

  For documentation for the rest of the parameters, see `scipy.optimize.linprog`

  Options:
    ——-
    maxiter int

      The maximum number of iterations to perform in either phase. Default is the largest possible value for an `int` on the platform.

    disp bool (default: `False`)

      Set to `True` if indicators of optimization status are to be printed to the console during optimization.

    presolve bool (default: `True`)

      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.

    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.

    dual_feasibility_tolerance double (default: 1e-07)

      Dual feasibility tolerance for ‘highs-ds’.

    primal_feasibility_tolerance double (default: 1e-07)

      Primal feasibility tolerance for ‘highs-ds’.

    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, `None` always selects `'steepest-devex'`, but this may change as new options become available.

    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.

  Notes

  Method ‘highs-ds’ is a wrapper of the C++ high performance dual revised simplex implementation (HSOL) [13], [14]. Method ‘highs-ipm’ is a wrapper of a C++ implementation of an interior-point method [13]; it features a crossover routine, so it is as accurate as a simplex solver. Method ‘highs’ chooses between the two automatically. For new code involving `linprog`, we recommend explicitly choosing one of these three method values instead of ‘interior-point’ (default), ‘revised simplex’, and ‘simplex’ (legacy).

  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

  [13] (1,2)

  Huangfu, Q., Galabova, I., Feldmeier, M., and Hall, J. A. J. “HiGHS - high performance software for linear optimization.” https://highs.dev/

  [14]

  Huangfu, Q. and Hall, J. A. J. “Parallelizing the dual revised simplex method.” Mathematical Programming Computation, 10 (1), 119-142, 2018. DOI: 10.1007/s12532-017-0130-5

  [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.