Multivariable Chain Rules and Implicit Surfaces
Track every dependency through a composition and use a nonzero partial to solve locally for a variable.
Builds on Gradients and Directional Derivatives
The bigger question: How does a surface change in different directions?
On this page
Add contributions from every changing input
If and the relevant maps are differentiable, then
If depend on variables , apply the same rule separately for each input: . Evaluate outer derivatives at the inner point. The Jacobian matrix organizes these relations as , with compatible matrix dimensions.
Visual guide
- Sphere slice x² + z² = 5
- Tangent z = 2 − (x − 1)/2
Worked example: a path across a surface
Let , with and . The chain rule gives
Substituting first gives , whose ordinary derivative agrees. Agreement between the two routes is a useful check when both are manageable.
For a temperature field experienced by a moving sensor, the total rate is . The explicit time term is separate from motion through spatial variation.
Try it yourself.
Choose an answer and explain your reasoning to yourself. Use a hint if you get stuck.
Hint 1 · Find a starting point
Both x and y depend on t.
Hint 2 · Take the next step
Use fₓx′+fᵧy′, or first substitute f=t³.
Show the reasoning
Answer: 3t²
t²×1+t×2t=3t², matching the derivative of t³.
Before moving on: what would make one of the other answers wrong? Saying why is part of understanding.
Worked example: solve for a surface height
Suppose . Where , the implicit function theorem allows a local differentiable height , with
At , the slopes are and . The tangent plane is , equivalently .
At equator points where , this height formula fails because the sphere is vertical relative to the -plane. The sphere itself is still smooth there: another nonzero partial can provide a different local graph. Failure of one coordinate representation is not necessarily a singular surface.
Practice
- Find when .
- Find for where .
- What term is missing from when the field itself changes with time?
Show worked solutions
- .
- and , giving .
- The explicit partial derivative .
Further study
MIT OpenCourseWare: Multivariable Calculus provides a full university course with additional lectures and exercises.
Pause before the next idea.
Can you explain how the visualization connects to this lesson’s goal? If a step still feels uncertain, put this lesson on your review list and try the check again another day.