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Simplex Method Questions and Answers

1.

In the context of operations research, the "Simplex Method" is primarily associated with solving which type of problems?

Exam Year(s)

DSSSB - 2023,

Answer: A

The simplex method is used for solving linear programming problems. It moves from one corner point of the feasible region to the next, improving the objective function, until the optimum is found. Dynamic programming, network flow problems and non-linear optimisation have other methods of solution, so they are not the problems of the simplex method.

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2.

At the current basic feasible solution (bfs) \(\mathbf{v}_{0}\) (\(\mathbf{v}_{0} \in \mathbb{R}^{5}\)), the simplex method yields the following form of a linear programming problem in standard form.

minimize \(z = -x_{1} - 2x_{2}\)
s.t. \(x_{3} = 2 + 2x_{1} - x_{2}\)
\(x_{4} = 7 + x_{1} - 2x_{2}\)
\(x_{5} = 3 - x_{1}\)
\(x_{1}, x_{2}, x_{3}, x_{4}, x_{5} \ge 0\)

Here the objective function is written as a function of the non-basic variables. If the simplex method moves to the adjacent bfs \(\mathbf{v}_{1}\) (\(\mathbf{v}_{1} \in \mathbb{R}^{5}\)) that best improves the objective function, which of the following represents the objective function at \(\mathbf{v}_{1}\), assuming that the objective function is written in the same manner as above?

Exam Year(s)

GATE ME - 2024,

Answer: A

The objective at \(\mathbf{v}_{1}\) is \(z = -4 - 5x_{1} + 2x_{3}\).

Both non-basic variables have negative coefficients in z, so increasing either one improves it. Find how far each can rise (ratio test) and the resulting z:

  • Raising \(x_{2}\): \(x_{3} = 2 - x_{2} \ge 0\) gives \(x_{2} \le 2\), and \(x_{4} = 7 - 2x_{2}\) gives \(x_{2} \le 3.5\). So \(x_{2} = 2\), \(x_{3}\) leaves, and \(z = -4\).
  • Raising \(x_{1}\): only \(x_{5} = 3 - x_{1}\) limits it, so \(x_{1} = 3\) and \(z = -3\).

The best improvement is to bring in \(x_{2}\). From the \(x_{3}\) row, \(x_{2} = 2 + 2x_{1} - x_{3}\), and substituting:

\[ \begin{aligned} z &= -x_{1} - 2(2 + 2x_{1} - x_{3}) \\ &= -4 - 5x_{1} + 2x_{3} \end{aligned} \]

Option B is the objective after bringing in \(x_{1}\), the lesser improvement. Option C has the wrong leaving variable (\(x_{4}\) is not the binding constraint), and D has the wrong constant.

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