Use the simplex method and the Duality Principle to solve the following minimum problem: (see image below) and using your final tableau answer the questions below by entering the correct answer in each blank box.

Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
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Use the simplex method and the Duality Principle to solve the following minimum problem: (see image below)

and using your final tableau answer the questions below by entering the correct answer in each blank box. 

### Problem Statement

**Objective:**
Minimize the function: \( C = 2x_1 + 3x_2 \)

**Subject to the constraints:**
\[
\begin{align*}
4x_1 - x_2 & \geq 2 \\
x_1 + x_2 & \geq 1 \\
x_1 & \geq 0 \\
x_2 & \geq 0
\end{align*}
\]

### Explanation:
- **Objective Function**: The function \( C = 2x_1 + 3x_2 \) is the expression to be minimized.
- **Constraints**: These inequalities restrict the values of \( x_1 \) and \( x_2 \) to feasible solutions that satisfy all conditions.
  - The first inequality \( 4x_1 - x_2 \geq 2 \) represents a linear constraint.
  - The second inequality \( x_1 + x_2 \geq 1 \) is another linear constraint.
  - The last two constraints \( x_1 \geq 0 \) and \( x_2 \geq 0 \) ensure that the variables are non-negative, which is typical in linear programming problems. 

**Note**: The problem can be graphically interpreted by plotting the constraints on a Cartesian plane, where the feasible region will be the intersection of all half-planes represented by the inequalities. The optimal solution will be found at a vertex (corner point) of this feasible region.
Transcribed Image Text:### Problem Statement **Objective:** Minimize the function: \( C = 2x_1 + 3x_2 \) **Subject to the constraints:** \[ \begin{align*} 4x_1 - x_2 & \geq 2 \\ x_1 + x_2 & \geq 1 \\ x_1 & \geq 0 \\ x_2 & \geq 0 \end{align*} \] ### Explanation: - **Objective Function**: The function \( C = 2x_1 + 3x_2 \) is the expression to be minimized. - **Constraints**: These inequalities restrict the values of \( x_1 \) and \( x_2 \) to feasible solutions that satisfy all conditions. - The first inequality \( 4x_1 - x_2 \geq 2 \) represents a linear constraint. - The second inequality \( x_1 + x_2 \geq 1 \) is another linear constraint. - The last two constraints \( x_1 \geq 0 \) and \( x_2 \geq 0 \) ensure that the variables are non-negative, which is typical in linear programming problems. **Note**: The problem can be graphically interpreted by plotting the constraints on a Cartesian plane, where the feasible region will be the intersection of all half-planes represented by the inequalities. The optimal solution will be found at a vertex (corner point) of this feasible region.
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