## Introduction to Linear Programming: With Applications |

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algebra artificial vectors augmented matrix basic feasible solution basic solution Basis Obj basis vectors branch canonical form change of basis Chapter coefficients column space column vector consider constraints convex combination convex set corresponding cost defined definition denote determine dual problem enter the basis equations Example Exercise feasible function finite number following tableau form a basis Formulate Hence inequality integers inventory leave the basis line segment linear combination linear programming problem linearly independent maximal flow maximizing problem minimizing minimum nodes nonbasis vector number of units objective function obtain operations optimal basic feasible optimal solution path period proper sign prove real numbers related standard problem requirements row transformations satisfies scalar multiplication set of vectors sign for improvement signals per second simplex method simplex process simplex sequence solve Suppose terminal transportation problem transshipment problem unit vectors vector space