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Solução_Calculo_Stewart_6e

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F.<br />

192 ¤ CHAPTER 15 PARTIAL DERIVATIVES ET CHAPTER 14<br />

TX.10<br />

53.<br />

∂z<br />

∂r = ∂z ∂z ∂z<br />

cos θ + sin θ and<br />

∂x ∂y ∂θ = − ∂z ∂z<br />

r sin θ + r cos θ. Then<br />

∂x ∂y<br />

and<br />

∂ 2 z<br />

∂r 2 =cosθ ∂ 2 z<br />

∂x 2 cos θ +<br />

<br />

∂2 z<br />

∂y ∂x sin θ +sinθ<br />

=cos 2 θ ∂2 z<br />

+2cosθ sin θ<br />

∂2 z<br />

∂x2 ∂x∂y +sin2 θ ∂2 z<br />

∂y 2<br />

<br />

∂ 2 z<br />

∂z<br />

∂ 2<br />

2<br />

= −r cos θ<br />

∂θ ∂x +(−r sin θ) z<br />

∂x<br />

2<br />

(−r sin θ)+<br />

∂2<br />

∂ 2 z<br />

∂y 2 sin θ +<br />

<br />

z<br />

∂y ∂x r cos θ<br />

<br />

∂2 z<br />

∂x∂y cos θ<br />

−r sin θ ∂z <br />

∂ 2<br />

∂y + r cos θ z<br />

∂y r cos θ +<br />

∂2 z<br />

2 ∂x∂y (−r sin θ)<br />

= −r cos θ ∂z ∂z<br />

− r sin θ<br />

∂x ∂y + r2 sin 2 θ ∂2 z<br />

∂x − 2 2r2 cos θ sin θ<br />

∂2 z<br />

∂x∂y + r2 cos 2 θ ∂2 z<br />

∂y 2<br />

Thus<br />

∂ 2 z<br />

∂r + 1 ∂ 2 z<br />

2 r 2 ∂θ 2 + 1 ∂z<br />

r ∂r =(cos2 θ +sin 2 θ) ∂2 z<br />

− 1 r<br />

= ∂2 z<br />

∂x + ∂2 z<br />

as desired.<br />

2 ∂y2 ∂x + sin 2 θ +cos 2 θ ∂ 2 z<br />

2 ∂y 2<br />

∂z<br />

cos θ<br />

∂x − 1 ∂z<br />

sin θ<br />

r ∂y + 1 r<br />

<br />

cos θ ∂z<br />

∂x<br />

<br />

∂z<br />

+sinθ<br />

∂y<br />

55. (a) Since f is a polynomial, it has continuous second-order partial derivatives, and<br />

f(tx, ty) =(tx) 2 (ty)+2(tx)(ty) 2 +5(ty) 3 = t 3 x 2 y +2t 3 xy 2 +5t 3 y 3 = t 3 (x 2 y +2xy 2 +5y 3 )=t 3 f (x, y).<br />

Thus, f is homogeneous of degree 3.<br />

(b) Differentiating both sides of f(tx, ty) =t n f(x, y) with respect to t using the Chain Rule, we get<br />

∂<br />

∂t f(tx, ty) = ∂ ∂t [tn f(x, y)]<br />

⇔<br />

∂<br />

∂(tx)<br />

f(tx, ty) · + ∂<br />

∂(ty)<br />

f(tx, ty) · = x ∂<br />

∂<br />

f(tx, ty)+y<br />

∂(tx) ∂t ∂(ty) ∂t ∂(tx) ∂(ty) f(tx, ty) =ntn−1 f(x, y).<br />

Setting t =1: x ∂<br />

∂x f(x, y)+y ∂ f(x, y) =nf(x, y).<br />

∂y<br />

57. Differentiating both sides of f(tx, ty) =t n f(x, y) with respect to x using the Chain Rule, we get<br />

∂<br />

∂<br />

f(tx, ty) =<br />

∂x ∂x [tn f(x, y)]<br />

⇔<br />

∂<br />

∂ (tx)<br />

f(tx, ty) ·<br />

∂ (tx) ∂x + ∂<br />

∂ (ty)<br />

f(tx, ty) ·<br />

∂ (ty) ∂x = ∂<br />

tn<br />

∂x f(x, y) ⇔ tfx(tx, ty) =tn f x(x, y).<br />

Thus f x(tx, ty) =t n−1 f x(x, y).<br />

15.6 Directional Derivatives and the Gradient Vector ET 14.6<br />

1. We can approximate the directional derivative of the pressure function at K in the direction of S by the average rate of change<br />

of pressure between the points where the red line intersects the contour lines closest to K (extendtheredlineslightlyatthe<br />

left). In the direction of S, the pressure changes from 1000 millibars to 996 millibars and we estimate the distance between<br />

these two points to be approximately 50 km (using the fact that the distance from K to S is 300 km). Then the rate of change of<br />

pressure in the direction given is approximately<br />

996 − 1000<br />

50<br />

= −0.08 millibar/km.

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