Related Rates
This original textbook reading covers part of the AP topic. Follow the AP course outline for complete topic instruction and practice.
This section will discuss related rates. Related rates are changes in one thing with respect to another. When we covered optimization, everything was static. The volume of the can did not change. The area stayed the same. Now, everything is more dynamic.
Imagine you are pumping air in a balloon. If you have a balloon it might be handy to experience what I am talking about. As you pump air into the balloon, you are pumping a constant volume per second. However, the size of the balloon increases really quickly at first, and then slows down. The more air you pump into the balloon, the slower the balloon grows! To summarize,
constant
variable (Initially high, then slows down)
In this case, is not dependent on how much volume you are pumping per time. Instead, it depends on the radius itself. Whereas with optimization we only had one constraint, now we are given two. More specifically, we are given a constraint (volume) and an initial condition (the value of the radius, or another similar variable at that time). Suppose we wanted to compare the change in surface area of the balloon instead. We would have to be given the surface area at the time of interest. In this case, surface area would be the condition. Let’s try this out.
Example Problem 6.3.1
A droplet forming inside of a cloud has a diameter of 0.1 mm. The high humidity inside the cloud causes the rate of condensation to be 1 mm3/s.
a) At what rate does the diameter of the droplet increase after 3 seconds?
b) What was the original rate of increase of the droplet size when its diameter was still 0.1 mm?
Example Problem 6.3.2
Your friend wants you to time him to see how fast he can run. He starts out 10 m east of you, and after you hit the start button on the timer, he runs at a speed of 5 m/s north. How fast is the angle between you changing relative to the original starting point after 3 seconds? (See Interactive 6.3.1 at left).
Glossary and deeper reading · 2 refreshers
These editorial refreshers supplement the reading. Definitions and examples are added guidance; original source wording and recorded answers remain in their original context.
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Optimization
Optimization finds the greatest or least value of an objective while satisfying the problem’s constraints. Express the objective in suitable variables, determine the allowed domain, and compare all relevant candidates. For example, among rectangles with a fixed perimeter, writing area in terms of one side reduces the search to a one-variable problem. Endpoint and feasibility checks are part of the argument, not optional final details.
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Constraint in optimization
A constraint restricts the quantities or choices permitted in a problem. It can be an equation, an inequality, or a requirement from the physical context. For example, a rectangle with perimeter 20 must satisfy 2l+2w=20 with positive side lengths. Use constraints to determine the objective’s allowed domain before searching for extrema.
