bundles / scipy latest / scipy / optimize / _linprog_util / _clean_inputs
function
scipy.optimize._linprog_util:_clean_inputs
source: build-install/usr/lib/python3.14/site-packages/scipy/optimize/_linprog_util.py:213
Signature
def _clean_inputs(lp)Summary
Given user inputs for a linear programming problem, return the objective vector, upper bound constraints, equality constraints, and simple bounds in a preferred format.
Parameters
lp: A `scipy.optimize._linprog_util._LPProblem` consisting of the following fields:c
The coefficients of the linear objective function to be minimized.
A_ub
The inequality constraint matrix. Each row of
A_ubspecifies the coefficients of a linear inequality constraint onx.b_ub
The inequality constraint vector. Each element represents an upper bound on the corresponding value of
A_ub @ x.A_eq
The equality constraint matrix. Each row of
A_eqspecifies the coefficients of a linear equality constraint onx.b_eq
The equality constraint vector. Each element of
A_eq @ xmust equal the corresponding element ofb_eq.bounds
The bounds of
x, asminandmaxpairs. If bounds are specified for all N variables separately, valid formats are: * a 2D array (2 x N or N x 2); * a sequence of N sequences, each with 2 values. If all variables have the same bounds, a single pair of values can be specified. Valid formats are: * a sequence with 2 scalar values; * a sequence with a single element containing 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).x0
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
x0represents a basic feasible solution.
Returns
lp: A `scipy.optimize._linprog_util._LPProblem` consisting of the following fields:c
The coefficients of the linear objective function to be minimized.
A_ub
The inequality constraint matrix. Each row of
A_ubspecifies the coefficients of a linear inequality constraint onx.b_ub
The inequality constraint vector. Each element represents an upper bound on the corresponding value of
A_ub @ x.A_eq
The equality constraint matrix. Each row of
A_eqspecifies the coefficients of a linear equality constraint onx.b_eq
The equality constraint vector. Each element of
A_eq @ xmust equal the corresponding element ofb_eq.bounds
The bounds of
x, asminandmaxpairs, one for each of the N elements ofx. The N x 2 array contains lower bounds in the first column and upper bounds in the 2nd. Unbounded variables have lower bound -np.inf and/or upper bound np.inf.x0
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
x0represents a basic feasible solution.
Aliases
scipy.optimize._linprog_util._clean_inputs