When working with linear objective functions in optimization problems, the slope of the objective function can provide useful insights. The key is to match the slope of the objective function with the slope of the constraint or boundary line to maximize or minimize the objective. This technique is particularly useful in linear programming problems where the goal is to optimize a linear function subject to certain constraints. Always ensure that you equate the slopes carefully when solving such problems.
Solution: The slope of the objective function \( Z = \alpha x + \beta y \) is given by \( -\frac{\alpha}{\beta} \). To maximize \( Z \), the slope of the objective function must match the slope of the line passing through the points (5, 5) and (0, 20).
The slope of the line passing through (5, 5) and (0, 20) is:
\(\text{Slope} = \frac{20 - 5}{0 - 5} = -3\).
Equating this with the slope of the objective function:
\(-\frac{\alpha}{\beta} = -3 \implies \alpha = 3\beta\).
Thus, the correct answer is \( \alpha = 3\beta \).
The slope of the objective function \( Z = \alpha x + \beta y \) is given by the formula:
\[ \text{Slope of the objective function} = -\frac{\alpha}{\beta}. \] In order to maximize \( Z \), the slope of the objective function must match the slope of the line passing through the given points (5, 5) and (0, 20).
Step 1: Calculate the slope of the line passing through the points (5, 5) and (0, 20):
The slope of a line passing through two points \( (x_1, y_1) \) and \( (x_2, y_2) \) is given by the formula: \[ \text{Slope} = \frac{y_2 - y_1}{x_2 - x_1} \] Substituting the given points (5, 5) and (0, 20): \[ \text{Slope} = \frac{20 - 5}{0 - 5} = \frac{15}{-5} = -3. \]
Step 2: Equate the slope of the objective function with the slope of the line:
The slope of the objective function is \( -\frac{\alpha}{\beta} \), and we want it to be equal to the slope of the line, which is \( -3 \). Therefore, we have the equation: \[ -\frac{\alpha}{\beta} = -3. \]
Step 3: Solve for \( \alpha \):
Simplifying the equation: \[ \frac{\alpha}{\beta} = 3 \implies \alpha = 3\beta. \]
Conclusion: Thus, the correct answer is \( \alpha = 3\beta \).
Minimize Z = 5x + 3y \text{ subject to the constraints} \[ 4x + y \geq 80, \quad x + 5y \geq 115, \quad 3x + 2y \leq 150, \quad x \geq 0, \quad y \geq 0. \]
List-I | List-II | ||
A | Megaliths | (I) | Decipherment of Brahmi and Kharoshti |
B | James Princep | (II) | Emerged in first millennium BCE |
C | Piyadassi | (III) | Means pleasant to behold |
D | Epigraphy | (IV) | Study of inscriptions |