Q.Maximise and Minimise Z=3x−4y subject to x−2y≤0, −3x+y≤4, x−y≤6, x,y≥0.
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The Graphical Method for Linear Programming
When a linear programming problem has just two decision variables, x and y, you can solve it by drawing a picture. This is the graphical method, and it is the technique the CBSE Class-12 course expects you to use.
The idea
Each constraint is a linear inequality such as 2x+3y≤100. On the xy-plane its boundary is a straight line, and the inequality picks one side of that line (a half-plane). The points that satisfy all the constraints at once form a single region — the feasible region. Your job is to find the point inside this region that makes the objective function Z=ax+by largest or smallest.
The step-by-step procedure
- Draw each constraint line. Replace every inequality by an equation and plot the line, usually by finding where it meets the axes.
- Shade the correct side. Test a simple point (often the origin (0,0)) in the inequality. If it holds, the origin's side is the wanted half-plane; if not, take the other side. Always include the non-negativity conditions x≥0, y≥0, which keep you in the first quadrant.
- Identify the feasible region. It is the overlap of all the shaded half-planes — the region satisfying every constraint together.
- Find the corner (vertex) points. These are the points where the boundary lines cross. Read them off the graph or solve the two relevant lines simultaneously.
- Evaluate Z at every corner and pick the largest value (for a maximum) or the smallest (for a minimum).
The whole method rests on the Corner-Point Theorem: if an optimum exists, it occurs at a vertex of the feasible region. So you never test interior points — only the corners.
Bounded vs unbounded …
Graphical method. Optimise Z=3x−4y over
x−2y≤0,−3x+y≤4,x−y≤6,x,y≥0.
Rewrite as y≥2x, y≤3x+4, y≥x−6. The feasible region is unbounded with corner points (0,0), (0,4), (12,6).
Z(0,0)=0,Z(0,4)=−16,Z(12,6)=36−24=12.
Maximum: the half-plane 3x−4y>12 misses the region, so Zmax=12 at (12,6). …
On this unbounded region the maximum of Z=3x−4y is 12 at (12,6), while the minimum does not exist because Z falls without bound.
Set up the region
x−2y≤0,−3x+y≤4,x−y≤6,x,y≥0.
As boundaries: y≥2x (from x−2y≤0), y≤3x+4 (from −3x+y≤4), and y≥x−6 (from x−y≤6).
Corner points
- x=0 with −3x+y=4: (0,4).
- x=0, y=0: (0,0).
- x−2y=0∩x−y=6: from x=2y, 2y−y=6⇒y=6, x=12: (12,6).
Each satisfies every constraint, so the vertices are (0,0), (0,4), (12,6). The region opens upward (you can increase y forever between y=2x and y=3x+4), so it is unbounded.
Evaluate Z=3x−4y
| Vertex | Z |
|---|---|
| (0,0) | 0 |
| (0,4) | −16 |
| (12,6) | 12 |
Maximum vs minimum on an unbounded region …
Method: Graphical (Corner-Point) Method for a Two-Variable LPP
Once a problem is written as "optimise Z=ax+by subject to linear inequalities in x and y," this is the standard CBSE technique for finding the optimum.
Steps
Step 1: Draw every constraint as a line.
Replace each inequality by an equation and plot the line from its axis intercepts. Include x=0 and y=0.
Step 2: Shade the correct half-plane.
Test the origin (0,0) in each inequality: if it is satisfied, keep the origin's side; if not, take the other side. Non-negativity confines you to the first quadrant.
Step 3: Identify the feasible region.
It is the single region where all the shaded half-planes overlap.
Step 4: Find the corner points.
Each vertex is the intersection of two boundary lines, found by solving those two equations together. Keep a candidate only if it satisfies every other constraint -- an intersection that breaks a third inequality is not a corner of the region.
Step 5: Evaluate Z at each corner.
By the Corner-Point Theorem the optimum of a linear Z always occurs at a vertex, so compare Z at the corners and pick the largest value (for a maximum) or the smallest (for a minimum).
Applying it here (asking for BOTH a maximum and a minimum) …
Common Mistakes
Mistake 1: Reporting the smallest corner value as the minimum on an unbounded region.
Why it's wrong: the least corner value is −16 at (0,4), but along the open edge y=3x+4 the objective is Z=−9x−16→−∞, so no minimum exists. Correct approach: run the open-half-plane check 3x−4y<(least corner) -- since it meets the region, conclude the minimum does not exist.
Mistake 2: Sign slips when rewriting the inequalities for y. …
- Higher Secondary (+2 Stage) Examination 2025Set ANNUAL5 marksQ.Solve the following linear programming problem graphically: Find the maximum and minimum value of Z, where Z=x+2y, subject to the constraints: x+2y≥100, 2x−y≤0, 2x+y≤200, x≥0, y≥0.
›Reveal solutionSolution
Plot the feasible region from the four constraints, find its corner points, then evaluate Z=x+2y at each corner — the maximum/minimum of a linear objective over a bounded polygon always occurs at a corner.
Constraints: x+2y≥100, 2x−y≤0 (i.e. y≥2x), 2x+y≤200, x≥0, y≥0.
Finding the corner points by intersecting the boundary lines in pairs:
- x=0 with x+2y=100: gives (0,50).
- x+2y=100 with y=2x: substituting, x+4x=100⇒x=20,y=40: gives (20,40).
- y=2x with 2x+y=200: substituting, 2x+2x=200⇒x=50,y=100: gives (50,100).
- 2x+y=200 with x=0: gives (0,200).
(One can check each of these four points satisfies all the constraints simultaneously, and that they are the actual vertices of the bounded feasible region — a quadrilateral.)
Evaluating Z=x+2y at each corner:
Corner Z=x+2y (0,50) 0+100=100 (20,40) 20+80=100 (50,100) 50+200=250 (0,200) 0+400=400 … - Higher Secondary (+2 Stage) Examination 2023Set ANNUAL5 marksQ.A businessman plans to sell two types of special computers: a desktop model and a portable model, whose current prices are Rs 25,000 and Rs 40,000 respectively. He estimates that the total monthly demand for computers will not exceed 250 units. If the profit is Rs 4,500 on each desktop model and Rs 5,000 on each portable model, and he does not want to invest more than Rs 70,00,000 (seventy lakh rupees) in total, determine, by the graphical method, how many units of each type of computer the businessman should stock to obtain the maximum profit. What is the maximum profit?
›Reveal solutionSolution
This is a linear programming problem: set up the demand and investment constraints, plot the feasible region, and evaluate the profit at each corner point — the maximum occurs at (200,50) giving a profit of Rs 11,50,000.
Let x = number of desktop models, y = number of portable models to stock.
Objective (maximize profit): Z=4500x+5000y
Constraints:
- Demand: x+y≤250
- Investment: 25000x+40000y≤70,00,000, which simplifies (dividing by 5000) to 5x+8y≤1400
- Non-negativity: x≥0, y≥0
Corner points of the feasible region:
- (0,0)
- x-intercept of demand line: (250,0) — check investment: 5(250)=1250≤1400 ✓, so this is a genuine corner.
- y-intercept of investment line: 8y=1400⇒y=175, giving (0,175) — check demand: 175≤250 ✓ (this is the binding constraint here, since the demand line would allow y=250 but investment caps it at 175).
- Intersection of x+y=250 and 5x+8y=1400: from the first, x=250−y; substitute: 5(250−y)+8y=1400⇒1250+3y=1400⇒y=50, x=200. Point: (200,50). …
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