Q.Solve the following Linear Programming Problem graphically by using Iso-cost method: Maximize Subject to the constraints: and
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Start your 14-day free trial to unlock the full solution →We convert the constraints into equations, plot the feasible region, and then use the iso-cost line to find the corner point where is maximized. The maximum value is at .
Why the Iso-Cost Method Works
In linear programming, the objective function represents a family of parallel lines — each line corresponds to a different value of . As we slide this line outward (increasing ), the last point it touches in the feasible region gives the optimal solution. This is the iso-cost (or iso-profit) method: we draw one such line for a convenient , then shift it parallel until it just leaves the feasible region.
The key insight: because the feasible region is convex and the objective is linear, the optimum always occurs at a corner point (vertex) of the region. So we can either slide the line visually or simply evaluate at all vertices.
Step-by-Step Solution
1. Convert inequalities to equations and plot the lines
We have four constraints (including non-negativity):
- and (axes)
Let’s find intercepts for each line:
| Line | x-intercept | y-intercept |
|---|---|---|
| set : (not in first quadrant) | set : | |
| vertical line at | — |
Since , we only care about the first quadrant.
2. Determine the feasible region
We shade the half-planes that satisfy each inequality:
- For : test gives (true), so shade below the line .
- For : test gives (true), so shade below .
- For : shade left of .
- restricts to the first quadrant.
The feasible region is the polygon bounded by these lines and axes.
3. Find the corner points (vertices)
The vertices are intersections of boundary lines:
A: Intersection of and →
B: Intersection of and →
But check : is false. So is not feasible.
Instead, intersection of and →
C: Intersection of and → →
D: Intersection of and
Subtract second from first:
Then →
E: Intersection of and → →
F: Intersection of and →
But check ? is false. So is not feasible. …
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