The coefficients of the linear objective function to be minimized.
The inequality constraint matrix. Each row of A_ub
specifies the coefficients of a linear inequality constraint on x
.
The inequality constraint vector. Each element represents an upper bound on the corresponding value of A_ub @ x
.
The equality constraint matrix. Each row of A_eq
specifies the coefficients of a linear equality constraint on x
.
The equality constraint vector. Each element of A_eq @ x
must equal the corresponding element of b_eq
.
The bounds of x
, as min
and max
pairs. If bounds are specified for all N variables separately, valid formats are: * a 2D array (N x 2); * a sequence of N sequences, each with 2 values. If all variables have the same bounds, the bounds can be specified as a 1-D or 2-D array or sequence with 2 scalar values. If all variables have a lower bound of 0 and no upper bound, the bounds parameter can be omitted (or given as None). Absent lower and/or upper bounds can be specified as -numpy.inf (no lower bound), numpy.inf (no upper bound) or None (both).
Guess values of the decision variables, which will be refined by the optimization algorithm. This argument is currently used only by the 'revised simplex' method, and can only be used if :None:None:`x0` represents a basic feasible solution.
This namedtuple supports 2 ways of initialization: >>> lp1 = _LPProblem(c=[-1, 4], A_ub=[[-3, 1], [1, 2]], b_ub=[6, 4]) >>> lp2 = _LPProblem([-1, 4], [[-3, 1], [1, 2]], [6, 4])
Note that only c
is a required argument here, whereas all other arguments A_ub
, b_ub
, A_eq
, b_eq
, bounds
, x0
are optional with default values of None. For example, A_eq
and b_eq
can be set without A_ub
or b_ub
: >>> lp3 = _LPProblem(c=[-1, 4], A_eq=[[2, 1]], b_eq=[10])
Represents a linear-programming problem.
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