Q.Ramesh, the owner of a sweet selling shop, purchased some rectangular cardboard sheets of dimension cm by cm to make container packets without top. Let cm be the length of the side of the square to be cut out from each corner to give that sheet the shape of the container by folding up the flaps. Based on the above information answer the following questions.
(iii)(A) For what value of , the volume of each container is maximum? [2 Marks]
Concept understanding — Optimization Word Problem
Optimization Word Problems
Imagine planning a garden with 40 metres of fencing and wanting the largest rectangular area. A long, thin rectangle wastes space; a square feels roomier; somewhere in between lies the best shape. That is an optimisation problem — a fixed resource and a quantity to make as large (or as small) as possible.
Every optimisation word problem has the same skeleton: the best outcome — maximum area, minimum cost, largest volume, shortest time — under a constraint — limited material, a fixed budget, a given perimeter.
The Plan of Attack
The problem gives you a story, not a graph. Your job is to turn it into a single-variable function and find its peak or valley:
- Name the quantity to optimise — call it , and write it using variables.
- Find the constraint — a relation between those variables (e.g. "perimeter ").
- Reduce to one variable — use the constraint to eliminate the rest.
- Differentiate — solve to find the critical points.
- Confirm — use for a maximum or for a minimum.
- Answer the question asked — give the actual dimensions/cost, not just .
In board exams these problems almost always reduce to a quadratic or cubic. Once is written, the calculus is mechanical.
The Garden, Worked
40 m of fencing encloses a rectangle; maximise the area.
- Objective: .
- Constraint: , so .
- Reduce: , giving .
- Differentiate: .
- Confirm: , a maximum.
So m — a 10 m 10 m square.
A common slip: solving and stopping. Always check max vs min, and answer in the units asked.
The Common Families
| Problem type | Typical objective | Typical constraint |
|---|---|---|
| Garden / fence | Area | Perimeter |
| Box from a sheet | Volume | Sheet size fixed |
| Cylinder (can) | Volume | Surface area fixed |
| Profit | Revenue and cost given |
The one thing to remember: optimisation is modelling first. The calculus is easy — the real work is reading the problem, choosing your variable, and writing the correct expression for .
Optimisation word problems — maximising area, minimising cost, finding the largest volume — are the practical centrepiece of the NCERT Class 12 Application of Derivatives chapter, and CBSE boards test at least one such problem nearly every year. Students searching 'optimization problems class 12 with solutions' or 'maxima minima word problems for boards' will find this objective-constraint-reduce-differentiate framework is exactly the six-step method those solutions follow.
With , the volume is maximum at cm (Part a), and is a point of inflection since changes sign there (Part b).
Cutting a square of side from each corner and folding up gives a box of height , length and width .
- Volume.
Expand: , so
- First derivative.
(iii)(A). Maximum volume. Set and divide by :
So or . Because the width is cm we need , so is rejected and is the only feasible value. The second derivative is ; at , , confirming a maximum.
The volume is maximum when cm.
Concept understanding — Critical Points Analysis
Critical Points Analysis: Where Functions Change Direction
Hiking a mountain range, you reach peaks (highest spot around), valleys (bottoms), and flat stretches where the ground doesn't slope. These special locations — peaks, valleys, and flat spots — are critical points.
The Intuition
A function's graph is like that trail. At most points it is rising (positive slope) or falling (negative slope). At a critical point something changes: the slope becomes zero, or the slope doesn't exist (a sharp corner).
Throw a ball straight up: at the very top of its arc it stops for an instant before falling. Its velocity — the rate of change of height — is zero at that moment. That's a critical point.
The Precise Definition
A point in the domain of is a critical point if either:
Why Two Conditions?
Derivative equals zero catches the "flat" spots — peaks, valleys, horizontal plateaus — where the tangent line is horizontal.
Derivative does not exist catches sharp corners (like the tip of at ), vertical tangents, and cusps. Even without a zero slope, these can be peaks or valleys.
A common mistake: thinking every critical point is a maximum or minimum. Not true. A critical point could be a "saddle point" — flat but neither. For example, at has , yet the function just passes through with no extremum.
How to Find Critical Points
- Find the derivative .
- Solve — these are candidates.
- Check where does not exist — but only if exists there (the point must be in the domain).
- Collect all such -values.
Example 1: A Simple Polynomial
Let .
.
or . Since exists everywhere, the critical points are and .
Example 2: A Function with a Corner
Let . Here does not exist at (left derivative , right derivative ), and has no solutions. So the only critical point is .
is actually a minimum of — the sharp corner is a valley.
What Critical Points Tell Us
Critical points are candidates for local maxima, local minima, or neither — they just flag "something interesting might be happening." To classify one, use further analysis:
- First Derivative Test: does change sign around the point?
- Second Derivative Test: what is the concavity there (if exists)?
Every local maximum and minimum of a differentiable function occurs at a critical point, but not every critical point is an extremum. Critical points are necessary but not sufficient for extrema.
Critical points — where f'(x) = 0 or f'(x) doesn't exist — are the starting point for nearly every maxima-minima question in the NCERT Class 12 Application of Derivatives chapter, a heavily weighted unit in CBSE Class 12 board exams. Searches for 'critical points class 12 examples' or 'how to find local maxima and minima using derivatives' consistently point back to exactly this necessary-but-not-sufficient definition.
With , the volume is maximum at cm (Part a), and is a point of inflection since changes sign there (Part b).
Cutting a square of side from each corner and folding up gives a box of height , length and width .
- Volume.
Expand: , so
- First derivative.
(iii)(B). Point of inflection at . A point of inflection needs and a sign change of .
Check the sign of on either side: and . The second derivative changes from negative to positive, so concavity changes.
Yes — since changes sign at , the curve has a point of inflection there.
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