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 f(x,y)=x3−3x+y2f(x, y) = x^3 - 3x + y^2 have, and where are they?

6. Applying the discriminant

For f(x,y)=x3−3x+y2f(x, y) = x^3 - 3x + y^2 at the critical point (−1,0)(-1, 0), the Hessian entries are fxx=−6f_{xx} = -6, fxy=0f_{xy} = 0, fyy=2f_{yy} = 2. What is the classification?

7. Classifying the other candidate

For the same function at the critical point (1,0)(1, 0), with fxx=6f_{xx} = 6, fxy=0f_{xy} = 0, fyy=2f_{yy} = 2, what is the classification?

8. The saddle prototype

For f(x,y)=x2−y2f(x, y) = x^2 - y^2 at the origin, give the value of the discriminant DD, as a plain number (negative numbers like -4 allowed).

9. Reading a minimum's value

For f(x,y)=x3−3x+y2f(x, y) = x^3 - 3x + y^2, give the local minimum value, achieved at (1,0)(1, 0), as a plain number.

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