LINEAR PROGRAMMING

a₁ artificial variables artificial vectors assume basic feasible solution basis matrix basis vectors cells Chapter column components compute Consider contains convex combination convex set corresponding cost degeneracy denoted determine the vector discussed dual problem element enter the basis example extreme point finite number flow given Hence hyperplane identity matrix inequalities initial basic feasible inserted inverse iteration labeled linear programming problem linearly independent maximize minimum nodes non-negative nonsingular matrix Note objective function obtain optimal basic feasible optimal basic solution optimal solution original problem Phase positive primal-dual algorithm procedure removed revised simplex method satisfied set of constraints set of feasible simplex algorithm slack variable supporting hyperplane surplus variables Table tableau tion transportation problem unbounded solution unique unit upper bound vector to enter Vectors CB yield zero level аз Уті