Q.Solve the following Linear Programming Problem graphically: Maximise subject to the constraints
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Start your 14-day free trial to unlock the full solution →To maximise the objective function subject to the given linear constraints, we use the graphical method to identify the feasible region and its corner points. Evaluating at these points reveals the maximum value. The maximum value of is .
Linear Programming Problems (LPPs) involve optimising (maximising or minimising) a linear objective function subject to a set of linear inequalities called constraints. When there are only two decision variables (like and in this problem), the graphical method provides a clear and intuitive way to solve the LPP.
The core idea behind the graphical method is that the set of all points that satisfy all the given constraints forms a region in the -plane, known as the feasible region. This region is always a convex polygon (or unbounded region). A fundamental theorem of linear programming states that if an optimal solution exists, it will always occur at one of the corner points (vertices) of this feasible region. This is because the objective function, being linear, will have its maximum or minimum value at an extreme point of the feasible region.
Here's how we apply this method step-by-step:
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Convert Inequality Constraints to Equations for Plotting
To graph the boundaries of our feasible region, we first treat each inequality constraint as a linear equation. This allows us to draw straight lines on the -plane.
Our constraints are:
- (non-negativity constraints, meaning we are restricted to the first quadrant)
The corresponding equations for plotting are:
- Line 1 ():
- Line 2 ():
- Line 3 ():
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Plot the Lines on the Graph
For each equation, we find two points (typically the intercepts with the axes) to draw the line.
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For
- If , then . Point: .
- If , then . Point: . Plot the line passing through and .
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For
- If , then . Point: .
- If , then . Point: . Plot the line passing through and .
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For
- If , then . Point: .
- If , then . Point: . Plot the line passing through and .
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Determine the Feasible Region
Now, we need to identify the region that satisfies all the original inequalities, including and . A common technique is to test a point (like the origin ) for each inequality.
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For :
Test : . This is true. So, the feasible region for this constraint lies on the side of that includes the origin.
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For :
Test : . This is false. So, the feasible region for this constraint lies on the side of that does not include the origin.
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For :
Test : . This is true. So, the feasible region for this constraint lies on the side of that includes the origin.
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For :
These constraints restrict the feasible region to the first quadrant (where both and are non-negative).
The feasible region is the area that is simultaneously:
- Below or on ()
- Above or on ()
- Below or on ()
- In the first quadrant ()
By shading these regions on a graph, we find that the feasible region is a polygon.
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Identify the Corner Points of the Feasible Region
The corner points are the vertices of the polygon formed by the feasible region. These points are found by determining the intersections of the boundary lines.
Let's list the corner points:
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Point A: Intersection of (y-axis) and .
Substitute into .
So, .
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Point B: Intersection of (x-axis) and .
Substitute into .
So, .
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Point C: Intersection of (x-axis) and . …
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