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The Simplex Algorithm Basic Concepts and Principles The Simplex Algorithm whose invention is due to George Dantzig in and in earned him the National Medal of Science is the main method for solving linear programming problems. Thus, m is the number of constraits files of matrix A or dimension of vector b and n is the number of variables columns of matriz A or dimension of vector C. If P has optimal solution x k finite, we know from Linear Programming Theory that x k is contained into the extreme points subset of feasible points set, S. If it holds at a certain extreme point x k, then this x k then such point is the solution that we are looking for. Suppose that x, extreme point of S. Lets w another point from the feasible region S. The optimal solution, if there, should be between these points. Given an extreme point x i in the feasible set, we check if the Optimality criterion holds for it, if does then x i is the optimal solution founded.
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Region of feasible solutions is an empty set. Go to the solution of your problem. This is a necessary condition for solving the problem: the numbers on the right parts of the constraint system simpiox on-line be non-negative. This is a necessary condition for solving the problem: all constraints must be equations. Finding the initial basis and the value of the function F which corresponds to the found initial basis. If each equation has a basic variable, then they say that the system has a basis.
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