Optimization involves translating a contextual situation into an objective function and constraints, identifying the feasible domain, and using first- or second-derivative reasoning to locate and justify absolute maxima or minima at critical points and endpoints. Solutions interpret the resulting value and corresponding input in context, recognizing that a derivative of zero alone does not guarantee an optimum. The scope is limited to single-variable functions and standard algebraic, geometric, and applied models, excluding multivariable optimization and Lagrange multipliers.
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Optimization problems ask you to find the largest or smallest possible value of a quantity.
A reliable process is:
A farmer has m of fencing to enclose three sides of a rectangular field beside a river. What dimensions give the greatest possible area?
Let
Only three sides need fencing, so the fencing constraint is
Solve for :
The area of a rectangle is
Substitute :
or
This is the function we want to maximize.
Both dimensions must be nonnegative:
and
Therefore,
This interval is the feasible domain.
Differentiate the area function:
Set the derivative equal to zero:
The critical point is .
Evaluate the area at , , and :
The greatest area is , which occurs when .
Find the corresponding length:
Therefore, the farmer should make the field
for a maximum area of
Notice that only identifies a possible optimum. Comparing it with the endpoints confirms that it is the absolute maximum on the feasible domain.
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