Consider a Linear Programming (LP) problem with 6 basic feasible solutions including a unique optimal solution. Suppose that we use the simplex algorithm to solve the LP problem. The simplex algorithm starts from a basic feasible solution and at each iteration it moves to another basic feasible solution with a better objective function value than the current solution. We assume that the simplex algorithm begins at the worst feasible solution and at each pivot (iteration) it is equally likely to move to any better basic feasible solution. On average, how many iterations will be required by the simplex algorithm in order to find the optimal solution to the LP problem?

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Consider a Linear Programming (LP) problem with 6 basic feasible solutions including a unique optimal solution. Suppose that we use the simplex algorithm to solve the LP problem. The simplex algorithm starts from a basic feasible solution and at each iteration it moves to another basic feasible solution with a better objective function value than the current solution. We assume that the simplex algorithm begins at the worst feasible solution and at each pivot (iteration) it is equally likely to move to any better basic feasible solution. On average, how many iterations will be required by the simplex algorithm in order to find the optimal solution to the LP problem?

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