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Practice: Implicit differentiation

Six short questions. Answer each one in your head (or on paper) before opening the collapsible. Trying to retrieve the answer is where the learning sticks; rereading feels productive but does much less.

1. State the implicit-differentiation procedure in steps.

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(1) Treat y as an unknown function of x. (2) Differentiate both sides of the relation with respect to x. (3) Attach a dy/dx to every y term (that is the chain rule). (4) Solve the resulting equation for dy/dx algebraically. You never have to solve the original relation for y.

2. Why is d/dx(y²) = 2y · dy/dx rather than 2y?

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Because y is secretly a function of x, so is really (y(x))², a composition. The chain rule gives the outer derivative 2·y(x) times the inner derivative y'(x) = dy/dx. The dy/dx is the chain-rule factor that rides along on every y term; forgetting it is the number-one implicit-differentiation error.

3. Why is implicit differentiation “not a new technique”?

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Because it is just the chain rule applied to a relationship. Every y term is a composition (some function of y(x)), so differentiating it produces a dy/dx. The x terms differentiate normally. Collecting the dy/dx terms and solving is plain algebra. If you can do the chain rule, you can do this.

4. What extra rule do you need for a mixed term like xy?

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The product rule: d/dx(xy) = (1)·y + x·(dy/dx) = y + x·dy/dx. A term with both variables multiplied is a product, so it gets the product rule, and the y factor still contributes its dy/dx. Treating xy as a single variable’s derivative loses a term.

5. What is “related rates,” and how does it differ from finding dy/dx?

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Related rates is the time-based twin: when x and y both change over time t under a constraint, you differentiate the constraint with respect to t, linking their rates dx/dt and dy/dt. It is the same implicit-differentiation move, but you differentiate against time instead of x, so you get linked speeds rather than a slope.

6. Why don’t you solve the relation for y before differentiating?

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Usually you cannot (a tilted ellipse like x² + xy + y² = 7 has no clean y = f(x) form), and you do not need to. Implicit differentiation finds the slope directly from the relation as it stands, which is exactly its payoff: it does not care whether the relation can be untangled.

Try it yourself, part 1: find dy/dx implicitly

Section titled “Try it yourself, part 1: find dy/dx implicitly”

Pen and paper, about 7 minutes.

(a) The circle x² + y² = 169. Find dy/dx, then evaluate it at the point (5, 12) and check it is perpendicular to the radius there.

(b) The relation x·y² = 8 (which you would not want to solve for y). Find dy/dx. (Hint: the left side is a product.)

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(a) Differentiate both sides with respect to x:

2x + 2y·dy/dx = 0 -> dy/dx = -x/y

At (5, 12) (on the circle, since 25 + 144 = 169): dy/dx = -5/12. Check: the radius from the origin to (5, 12) has slope 12/5, and the tangent must be perpendicular to it, with slope the negative reciprocal -5/12. The implicit derivative matches the geometry.

(b) The left side x·y² is a product, so use the product rule (and the chain rule on ):

d/dx(x·y²) = (1)·y² + x·(2y·dy/dx) = y² + 2xy·dy/dx = 0

Solve: 2xy·dy/dx = -y², so dy/dx = -y²/(2xy) = -y/(2x). No need to untangle y from the relation.

About 4 minutes. A spherical balloon is being inflated. Its volume and radius are tied by V = (4/3)π·r³, and both change with time. The radius is growing at dr/dt = 2 cm/s. How fast is the volume growing when the radius is r = 5 cm?

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Differentiate the constraint with respect to time t (the term needs the chain rule, depositing a dr/dt):

dV/dt = (4/3)π · 3r² · dr/dt = 4π·r²·dr/dt

Plug in r = 5 and dr/dt = 2:

dV/dt = 4π · (5)² · 2 = 4π · 25 · 2 = 200π ≈ 628 cm³/s

The volume is growing at 200π cubic cm per second at that instant. Notice the volume’s rate depends on the current radius (through the factor): the bigger the balloon, the faster its volume grows for the same dr/dt, because surface area grows with . The constraint links the two rates, and differentiating it with respect to time is what extracts the link.

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Q. What is the implicit-differentiation procedure?
A.

Treat y as a function of x, differentiate both sides with respect to x, attach a dy/dx to every y term (chain rule), then solve algebraically for dy/dx. You never solve the original relation for y.

Q. Why is d/dx(y²) = 2y·dy/dx?
A.

Because y is a function of x, so is the composition (y(x))². The chain rule gives 2·y(x) times the inner derivative dy/dx. The dy/dx factor rides on every y term; dropping it is the number-one error.

Q. Find dy/dx for the circle x² + y² = 25.
A.

Differentiate: 2x + 2y·dy/dx = 0, so dy/dx = -x/y. At (3,4) that is -3/4, perpendicular to the radius (slope 4/3), exactly the tangent slope.

Q. Why is implicit differentiation just the chain rule?
A.

Every y term is a composition (a function of y(x)), so differentiating it deposits a dy/dx. The x terms differentiate normally. Collecting dy/dx terms and solving is plain algebra. No new technique.

Q. How do you differentiate a mixed term like xy implicitly?
A.

With the product rule: d/dx(xy) = y + x·dy/dx. The y factor still carries its dy/dx. Skipping the product rule on mixed terms loses a term.

Q. How does implicit differentiation produce the derivative of ln(x)?
A.

From e^y = x (which says y = ln x): differentiate to get e^y·dy/dx = 1, so dy/dx = 1/e^y = 1/x (since e^y = x). The derivative of ln x is 1/x, with no special log rule needed.

Q. What is related rates?
A.

The time twin of implicit differentiation: when x and y both change with time under a constraint, differentiate the constraint with respect to t to link dx/dt and dy/dt. Same move, differentiated against time instead of x.

Q. Sliding ladder x² + y² = 100: how are the rates linked?
A.

Differentiate w.r.t. time: 2x·dx/dt + 2y·dy/dt = 0, so dy/dt = -(x/y)·dx/dt. If the base slides out at dx/dt = 1, the top falls faster as x grows: at (6,8), -0.75 ft/s; at (8,6), -1.33 ft/s.

Q. Where does implicit differentiation show up in machine learning?
A.

In methods using constraints or fixed points rather than explicit formulas: constrained optimization (Lagrange/KKT), deep equilibrium models (a layer defined as a fixed point f(z,x)=z), and differentiating through the ODEs in score-based diffusion.