Multivariable Calculus · 免费预览
临界点与二阶导数判别法
二元函数的局部极大值与极小值、梯度为零的临界点,以及用于对它们进行分类的黑塞矩阵判别式。
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.