Exam II - November 3, 2006, Multivariable Calculus - Prof. P. Wong, Exams of Mathematics

The solutions to exam ii of the multivariable calculus course taught by prof. P. Wong, held on november 3, 2006. Problems related to finding the divergence, curl, and jacobian matrix of vector fields, as well as problems involving directional derivatives, tangent planes, and critical points.

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MATH206A MULTIVARIABLE CALCULUS - PROF. P.
WONG
EXAM II - NOVEMBER 3, 2006
NAME:
Instruction: Read each question carefully. Explain ALL your work and
give reasons to support your answers.
Advice: DONโ€™T spend too much time on a single problem.
Problems Maximum Score Your Score
1. 20
2. 20
3. 20
4. 20
5. 20
Total 100
1
pf3
pf4
pf5

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MATH206A MULTIVARIABLE CALCULUS - PROF. P.

WONG

EXAM II - NOVEMBER 3, 2006

NAME:

Instruction: Read each question carefully. Explain ALL your work and give reasons to support your answers. Advice: DONโ€™T spend too much time on a single problem.

Problems Maximum Score Your Score

  1. 20
  2. 20
  3. 20
  4. 20
  5. 20 Total 100

1

2 EXAM II - NOVEMBER 3, 2006

  1. Let F : R^3 โ†’ R^3 be given by F (x, y, z) = (yz, xz, xy). (5 pts) (i) Find div F. For the given F , we have โˆ‚F โˆ‚x = โˆ‚F โˆ‚y = โˆ‚F โˆ‚z = 0. Since

div F = โˆ‡ ยท F = โˆ‚F โˆ‚x + โˆ‚F โˆ‚y + โˆ‚F โˆ‚z ,

it follows that div F = 0.

(5 pts) (ii) Find curl F

curl F = โˆ‡ ร— F = (x โˆ’ x, โˆ’(y โˆ’ y), z โˆ’ z) = (0, 0 , 0).

(5 pts) (iii) What is the Jacobian matrix DF (1, 1 , 1) of F at (1, 1 , 1)?

DF (1, 1 , 1) =

0 z y z 0 x y x 0

(5 pts) (iv) Find an approximation of F (0. 9 , 1. 1 , 1 .1). The linear approximation asserts that F (x) = F (a) + DF (a) ยท (x โˆ’ a)

where a = (1, 1 , 1). It follows that

F (0. 9 , 1. 1 , 1 .1) โ‰ˆ

4 EXAM II - NOVEMBER 3, 2006

  1. Consider the function f (x, y, z) = x^3 y โˆ’ yz^2 + z^5. (8 pts) (i) Find the directional derivative Duf (1, 1 , 0) of f at the point (1, 1 , 0) in the direction of u = i โˆ’ j + 3k. First, the gradient of f is given by โˆ‡f = (3x^2 y, x^3 โˆ’ z^2 , โˆ’ 2 yz + 5z^4 ) so that โˆ‡f (1, 1 , 0) = (3, 1 , 0). It follows that the directional derivative of f in the direction of u is given by

Duf (1, 1 , 0) = โˆ‡f (1, 1 , 0) ยท (^) โ€–uuโ€–

= (3,^1 ,^ 0) โˆš^ ยท^ (1,^ โˆ’^1 ,^ 3) 11 = โˆš^2 11

(12 pts) (ii) Find an equation for the plane tangent to the level surface f (x, y, z) = 9 at the point (3, โˆ’ 1 , 2). Since f (x, y, z) = 9 is a level surface, we know that โˆ‡f (3, โˆ’ 1 , 2) = (โˆ’ 27 , 23 , 84) is normal to the plane tangent to the level surface at the point (3, โˆ’ 1 , 2). Thus, this tangent plane is given by the equation (x โˆ’ 3 , y + 1, z โˆ’ 2) ยท (โˆ’ 27 , 23 , 84) = 0.

In other words, an equation for this tangent plane is

27 x โˆ’ 23 y โˆ’ 84 z + 64 = 0.

MATH206A MULTIVARIABLE CALCULUS - PROF. P. WONG 5

  1. Consider the function f (x, y) = 4y โˆ’ y^3 โˆ’ x^2. (7 pts) (i) Find all the critical points of f. The critical points of a smooth function f are precisely the points at which the gradient vanishes. Since โˆ‡f = (โˆ’ 2 x, 4 โˆ’ 3 y^2 ), it follows that the critical points are (0, โˆš^23 ) and (0, โˆ’ โˆš^23 ).

(6 pts) (ii) For each of the critical point(s) a found in part (i), find the corresponding Hessian matrix Hf (a). First, the Hessian matrix is given by

Hf =

[

0 โˆ’ 6 y

]

It follows that

Hf ((0, โˆš^2 3

[

0 โˆ’ โˆš^123

]

and Hf ((0, โˆ’ โˆš^2 3

[

0 โˆš^123

]

(7 pts) (iii) Use the second derivative test to classify each of the critical point(s) in part (i), i.e., determine whether the critical point is a local max, local min, or saddle point. At the critical point (0, โˆš^23 ),

det Hf ((0, โˆš^23 )) =^ โˆš^243 > 0 and โˆ‚

(^2) f โˆ‚x^2 =^ โˆ’^2.

This implies that (0, โˆš^23 ) is a local maximum for f. Similarly, at the critical point (0, โˆ’ โˆš^23 ),

det Hf ((0, โˆ’ โˆš^23 )) = โˆ’ โˆš^243 < 0.

It follows that (0, โˆ’ โˆš^23 ) is a saddle point for f.