`scipy.optimize._linprog_util:_postsolve`

source: build-install/usr/lib/python3.14/site-packages/scipy/optimize/_linprog_util.py:1314

Signature

def _postsolve(x, postsolve_args, complete = False)

Summary

Given solution x to presolved, standard form linear program x, add fixed variables back into the problem and undo the variable substitutions to get solution to original linear program. Also, calculate the objective function value, slack in original upper bound constraints, and residuals in original equality constraints.

Parameters

x : 1-D array

Solution vector to the standard-form problem.

postsolve_args : tuple

Data needed by _postsolve to convert the solution to the standard-form problem into the solution to the original problem, including:

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_ub specifies the coefficients of a linear inequality constraint on x.
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_eq specifies the coefficients of a linear equality constraint on x.
b_eq: The equality constraint vector. Each element of A_eq @ x must equal the corresponding element of b_eq.
bounds: The bounds of x, lower bounds in the 1st column, upper bounds in the 2nd column. The bounds are possibly tightened by the presolve procedure.
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 x0 represents a basic feasible solution.

revstack: list of functions

the functions in the list reverse the operations of _presolve() the function signature is x_org = f(x_mod), where x_mod is the result of a presolve step and x_org the value at the start of the step

complete : bool

Whether the solution is was determined in presolve (True if so)

Returns

x : 1-D array: Solution vector to original linear programming problem
: fun: float: optimal objective value for original problem
slack : 1-D array: The (non-negative) slack in the upper bound constraints, that is, b_ub - A_ub @ x
con : 1-D array: The (nominally zero) residuals of the equality constraints, that is, b - A_eq @ x

Aliases

scipy.optimize._linprog._postsolve