A1 LINEAR PROGRAMMING AND OPTIMAL SOLUTIONS A2
A nonnegative vector of variables that satisfies the constraints of (P) is called a feasible solution to the linear programming problem A feasible solution that minimizes the objective function is called an optimal solution
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Second, the simplex method provides much more than just optimal solutions limited and restrictive; as we will see later, however, any linear programming In the example above, the basic feasible solution x1 = 6, x2 = 4, x3 = 0, x4 = 0,
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If an LP has an optimal solution, then it has an optimal solution at an extreme point of the feasible set Proof Idea: If the optimum is not extremal, it's on some line
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Optimal solution to a L P P: A feasible solution to a L P P which makes the objective function optimal Basic Solutions of a set of Linear Simultaneous Equations
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Let us formulate a linear program that will lead us to the optimal solution linear programming algorithm that searches through basic feasible solutions
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basic feasible solution: put the slack variables on the left hand side How- ever, this is Negating that we get that the optimal objective function value is 5, as we
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Let us return to the linear programming problem P The fundamental result is that we need only search among the basic feasible solutions for an optimal solution
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The decision variables are the n-dimensional vector x Note that the objective can be minimization or maximization Combinatorial Optimization Problem: A
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Basics on Linear Programming Combinatorial A feasible LP with no optimal solution is unbounded basic feasible solution, and the basis is feasible
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Given an LP, how do we find its optimal solution? A basic feasible solution of a linear program with n variables is a feasible solution equal to the solution of a
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