Math 2204 Multivariable Calculus – Chapter 14: Partial Derivatives Sec. 14.6: Directional Derivatives and Gradient Vectors I. Directional Derivatives A. What if you want to calculate the slope at any point moving in any direction, not just in the direction of x or y. 1. Find the slope moving in NE direction from (4,-6) -8 -6 -4 2 80 140 209 4 71 124 186 6 58 102 152 2. In general, slope from ( x 1 , y 1 ) to ( x 2 , y 2 ) is m = f ( x 2 , y 2 ) − f ( x 1 , y 1 ) ( x 2 − x 1 ) 2 + ( y 2 − y 1 ) 2 B. Definition The derivative of f at P 0 ( x 0 , y 0 ) in the direction of the unit vector u = u 1 i + u 2 j is the number D ! u f ( P 0 ) = lim h→0 f ( x 0 + hu 1 , y 0 + hu 2 ) − f ( x 0 , y 0 ) h ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ , provided the limit exists. C. Theorem If f is a differentiable function of x and y, then f has a directional derivative in the direction of any unit vector u = u 1 i + u 2 j and D u f ( x, y) = f x ( x, y)u 1 + f y ( x, y)u 2 D. Example: Find the directional derivative D u f ( x, y) if f ( x, y) = x 3 − 3xy + 4 y 2 in the direction of v = 3 i + j at the point (1,2). E ↓ N →
7
Embed
Math 2204 Multivariable Calculus – Chapter 14: Partial ... · Math 2204 Multivariable Calculus – Chapter 14: Partial Derivatives Sec. 14.6: Directional Derivatives and Gradient
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Math 2204 Multivariable Calculus – Chapter 14: Partial Derivatives Sec. 14.6: Directional Derivatives and Gradient Vectors
I . Directional Derivatives
A. What if you want to calculate the slope at any point moving in any direction, not just in the direction of x or y.
1. Find the slope moving in NE direction from (4,-6)
-8 -6 -4 2 80 140 209 4 71 124 186 6 58 102 152
2. In general, slope from (x1, y1) to (x2 , y2 ) is m =f (x2 , y2 ) − f (x1, y1)(x2 − x1)
2 + (y2 − y1)2
B. Definition The derivative of f at P0 (x0 , y0 ) in the direction of the unit vector
u = u1i + u2
j is the
number D!u f (P0 ) = limh→0f (x0 + hu1, y0 + hu2 )− f (x0, y0 )
h⎛⎝⎜
⎞⎠⎟ , provided the limit exists.
C. Theorem If f is a differentiable function of x and y, then f has a directional derivative in the direction of any unit vector
u = u1i + u2
j and D
u f (x, y) = fx (x, y)u1 + fy (x, y)u2
D. Example: Find the directional derivative Du f (x, y) if f (x, y) = x
3 − 3xy + 4y2 in the
direction of v = 3
i +j at the point (1,2).
E↓
N →
II. Gradient Vectors A. Definition
The gradient vector (gradient) of f(x,y) at a point P0 (x0 , y0 ) is the vector ∇f =
∂f∂xi +
∂f∂yj
obtained by evaluating the partial derivatives of f at P0 . B. The notation ∇f is read as “del f” or “grad f” or “gradient of f” C. Theorem: The Directional Derivative is a Dot Product
If f(x,y) is differentiable in an open region containing P0 (x0 , y0 ) , then D!u f (x0, y0 ) = ∇f (P0 ) ⋅
!u , the dot product of the gradient f at P0 and u .
D. Algebra Rules for Gradients 1. Constant Multiple Rule: ∇(kf ) = k∇f , (any number k)
2. Sum/Difference Rule: ∇( f ± g) = ∇f ±∇g
3. Product Rule: ∇( fg) = f∇g ± g∇f
4. Quotient Rule: ∇fg
⎛⎝⎜
⎞⎠⎟=g∇f − f∇g
g2
E. The gradient vector points in the direction in which f increases most rapidly. F. The vector’s magnitude is the rate of change in that direction. G. If the directional derivative of f at (a,b) is zero in every direction, ∇f = 0 . H. At every point (x0 , y0 ) in the domain of a differentiable function f(x,y), the gradient of f is
normal to the level curve through (x0 , y0 ) .
I. The gradient vector is normal to the tangent line. J. The gradient doesn’t necessarily point to the steepest point (peak); it goes to the next
contour in the shortest distance.
K. ∇f is large when contours are close together and small when they are far apart.
L. Examples
1. Find the derivative of F(x, y) = 3x2 − xy + y2 in the direction of u = 1
2
i + 1
2
j at (1,5).
2. Given F(x, y) = 2 + 1
2 x − y and the point P0 (2,0) .
a. Find the derivative in the direction of v =i + 2
j at P0 .
b. Find the equation of the tangent line at P0 . 3. Find the derivative of F(x, y, z) = xy + ln z in the direction of
v =i + 4
k at (2,-1,1).
III. Properties of the Directional Derivative Du f = (∇f ) ⋅u = ∇f cosθ
A. Properties 1. The function f increases most rapidly when cosθ = 1 or θ = 0 or when
u is the direction of ∇f . That is, at each point P in its domain, f increases most rapidly in the direction of the gradient vector ∇f at P. The derivative in this direction is
Du f = ∇f cos0 = ∇f .
2. The function f decreases most rapidly when cosθ = −1 or θ = π or when u is the
direction of −∇f . The derivative in this direction is Du f = ∇f cosπ = − ∇f .
3. Any direction u orthogonal to a gradient ∇f ≠ 0 is a direction of zero change in f
because cosθ = 0 or θ =π2
and Du f = ∇f cosπ
2= ∇f ⋅0 = 0
B. Example 1. Suppose T (x, y) = 70 + xy represents level curves around a heat source. Let P(2,−1)
be a point where you are sitting. a. How hot is it? b. What is the equation of the level curve you are on? c. If you move in the direction of
v =< −1,1 > , is it getting hotter or cooler?
The direction in which f(x,y) increases most rapidly at (1,1) is the direction of
∇f (1,1) =
i +j . (the direction of
steepest ascent on the surface at (1,1,1))
d. Find the direction of maximum temperature increase/ decrease. e. What is the rate? f. What is the direction if you wish to stay at 680 ? g. Find the direction in which Du f = 1 .
IV. Tangent Planes and Normal Line A. Definitions 1. The tangent plane at the point P0 (x0 , y0 , z0 ) on the level surface f (x, y, z) = c of a
differentiable function f is the plane through P0 normal to ∇f P0 .
2. The normal line of the surface at P0 is the line through P0 parallel to ∇f P0 .
B. Equations 1. Tangent Plane to f (x, y, z) = c at P0 (x0 , y0 , z0 )