Functions and Graphs · Topic 6 of 7
6. Optimisation
Theory
An optimisation problem asks for the largest or smallest value something can take — the greatest volume, the least cost, the shortest distance. The calculus is the easy part; the skill is turning the words into a function first.
Almost every such problem starts with two variables and a constraint linking them (a fixed perimeter, a fixed volume). Use the constraint to eliminate one variable, so the quantity to be optimised becomes a function of a single variable. Then differentiate, set equal to zero, and confirm the nature of the stationary point:
A negative second derivative confirms a maximum, a positive one a minimum.
The Golden Rule: the modelling comes before the calculus. Write down the quantity to optimise, write down the constraint, and use the constraint to get the quantity in terms of one variable — only then differentiate. And always confirm the nature of the stationary point; a question about a maximum expects you to show it is one.
⚠️ Common Examiner Traps
- Trying to differentiate two variables: you must use the constraint to eliminate one first, or there is nothing to differentiate with respect to.
- Not justifying the nature: finding a stationary point is not enough — use the second derivative (or a nature table) to show it really is the maximum or minimum asked for.
- Ignoring the domain: a length or radius must be positive, so reject negative or zero stationary values, and check the endpoints if the variable is restricted.
- Answering the wrong question: if asked for the maximum volume, substitute the optimal back into the volume — do not stop at the value of itself.
Worked examples
Example 1
A farmer has m of fencing to make a rectangular enclosure against an existing straight wall, so only three sides need fencing. Find the dimensions that give the greatest area, and state that area.
Step 1: Let the two sides perpendicular to the wall be and the side parallel to it be . Only three sides are fenced, so the constraint is:
Step 2: The quantity to maximise is the area. Write it using the constraint to remove :
Step 3: Differentiate and set to zero:
Step 4: Confirm it is a maximum:
Step 5: Find and the area:
The enclosure is m by m, giving a maximum area of m².
Example 2
A closed cylindrical can is to hold cm³. Find the radius that minimises the total surface area.
Step 1: The two variables are the radius and height . The fixed volume is the constraint:
Step 2: The quantity to minimise is the total surface area (two ends plus the curved side):
Step 3: Substitute the constraint to get in terms of alone:
Step 4: Differentiate, writing the second term as , and set to zero:
Step 5: Confirm it is a minimum using the second derivative, which is positive for all :
Example 3
An open-topped box is made from a square sheet of card of side cm by cutting a square of side from each corner and folding up the flaps. Find the value of that maximises the volume.
Step 1: After cutting corners of side and folding up, the base is a square of side and the height is . So the volume is:
Step 2: Expand ready to differentiate:
Step 3: Differentiate and set to zero:
Step 4: Factorise and solve:
Step 5: Reject : it would make the base side , so no box. Check is a maximum:
Step 6: So cm gives the maximum volume, which is cm³.