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Basic Concepts in Linear Programming
Model Building with Linear Programming
Chapter S The Simplex Algorithm
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activity artificial variables assignment assignment problem augmented matrix basic sequence basic variables called canonical form codomain coefficients components consider constraints corresponding cost matrix crude defined denote determine domain dual problem dual vector duality elements equivalence relation example feasible program feasible vector Figure given hence illustrated indicate inequalities infeasible initial tableau input integers introduce linear equations linear programming problem loop mathematical maximize minimize mixed strategy negative nonbasic variables nonnegative nonzero notation number of units objective function obtain Oleum optimal feasible optimal program optimal solution original problem orthogonal output partition pivot operation player primal and dual primal vector probability vector profit properties pure strategies real numbers saddle point satisfies scalar schema Section simplex algorithm simplex method slack variables Solve strategy subset Suppose symmetric Table tableau matrix theorem transportation problem vector space zero