Multivariable Calculus · free preview
Critical Points and the Second-Derivative Test
Local maxima and minima of functions of two variables, critical points where the gradient vanishes, and the Hessian discriminant that classifies them.
1. Extrema force the gradient to vanish
Extrema force the gradient to vanish
2. The Hessian discriminant classifies each candidate
The Hessian discriminant classifies each candidate
3. Saddles are the new phenomenon
Saddles are the new phenomenon
4. The test's silent case and its domain
The test's silent case and its domain
5. Finding the critical points
How many critical points does have, and where are they?
6. Applying the discriminant
For at the critical point , the Hessian entries are , , . What is the classification?
7. Classifying the other candidate
For the same function at the critical point , with , , , what is the classification?
8. The saddle prototype
For at the origin, give the value of the discriminant , as a plain number (negative numbers like -4 allowed).
9. Reading a minimum's value
For , give the local minimum value, achieved at , as a plain number.
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