partial derivatives 11. partial derivatives 11.6 directional derivatives and the gradient vector in...
TRANSCRIPT
![Page 1: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/1.jpg)
PARTIAL DERIVATIVESPARTIAL DERIVATIVES
11
![Page 2: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/2.jpg)
PARTIAL DERIVATIVES
11.6Directional Derivatives
and the Gradient Vector
In this section, we will learn how to find:
The rate of changes of a function of
two or more variables in any direction.
![Page 3: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/3.jpg)
INTRODUCTION
This weather map shows a contour map
of the temperature function T(x, y)
for:
The states of California and Nevada at 3:00 PM on a day in October.
![Page 4: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/4.jpg)
INTRODUCTION
The level curves, or isothermals,
join locations with the same
temperature.
![Page 5: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/5.jpg)
The partial derivative Tx is the rate of change
of temperature with respect to distance if
we travel east from Reno.
Ty is the rate of change if we travel north.
INTRODUCTION
![Page 6: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/6.jpg)
However, what if we want to know the rate
of change when we travel southeast (toward
Las Vegas), or in some other direction?
INTRODUCTION
![Page 7: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/7.jpg)
In this section, we introduce a type of
derivative, called a directional derivative,
that enables us to find:
The rate of change of a function of two or more variables in any direction.
DIRECTIONAL DERIVATIVE
![Page 8: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/8.jpg)
DIRECTIONAL DERIVATIVES
Recall that, if z = f(x, y), then the partial
derivatives fx and fy are defined as:
0 0 0 00 0
0
0 0 0 00 0
0
( , ) ( , )( , ) lim
( , ) ( , )( , ) lim
xh
yh
f x h y f x yf x y
h
f x y h f x yf x y
h
Equations 1
![Page 9: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/9.jpg)
DIRECTIONAL DERIVATIVES
They represent the rates of change of z
in the x- and y-directions—that is, in
the directions of the unit vectors i and j.
Equations 1
![Page 10: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/10.jpg)
DIRECTIONAL DERIVATIVES
Suppose that we now wish to find the rate
of change of z at (x0, y0) in the direction of
an arbitrary unit vector u = <a, b>.
![Page 11: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/11.jpg)
DIRECTIONAL DERIVATIVES
To do this, we consider the surface S
with equation z = f(x, y) [the graph of f ]
and we let z0 = f(x0, y0).
Then, the point P(x0, y0, z0) lies on S.
![Page 12: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/12.jpg)
DIRECTIONAL DERIVATIVES
The vertical plane that passes through P
in the direction of u intersects S in
a curve C.
![Page 13: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/13.jpg)
DIRECTIONAL DERIVATIVES
The slope of the tangent line T to C
at the point P is the rate of change of z
in the direction
of u.
![Page 14: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/14.jpg)
DIRECTIONAL DERIVATIVES
Now, let:
Q(x, y, z) be another point on C.
P’, Q’ be the projections of P, Q on the xy-plane.
![Page 15: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/15.jpg)
DIRECTIONAL DERIVATIVES
Then, the vector is parallel to u.
So,
for some scalar h.
' '��������������P Q
' '
,
P Q h
ha hb
u
��������������
![Page 16: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/16.jpg)
DIRECTIONAL DERIVATIVES
Therefore,
x – x0 = ha
y – y0 = hb
![Page 17: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/17.jpg)
DIRECTIONAL DERIVATIVES
So,
x = x0 + ha
y = y0 + hb
0
0 0 0, 0( , ) ( )
z zz
h hf x ha y hb f x y
h
![Page 18: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/18.jpg)
DIRECTIONAL DERIVATIVE
If we take the limit as h → 0, we obtain
the rate of change of z (with respect to
distance) in the direction of u.
This is called the directional derivative of f in the direction of u.
![Page 19: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/19.jpg)
DIRECTIONAL DERIVATIVE
The directional derivative of f at (x0, y0)
in the direction of a unit vector u = <a, b>
is:
if this limit exists.
Definition 2
0 0
0 0 0 0
0
( , )
( , ) ( , )limh
D f x y
f x ha y hb f x y
h
u
![Page 20: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/20.jpg)
DIRECTIONAL DERIVATIVES
Comparing Definition 2 with Equations 1,
we see that:
If u = i = <1, 0>, then Di f = fx.
If u = j = <0, 1>, then Dj f = fy.
![Page 21: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/21.jpg)
DIRECTIONAL DERIVATIVES
In other words, the partial derivatives of f
with respect to x and y are just special
cases of the directional derivative.
![Page 22: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/22.jpg)
DIRECTIONAL DERIVATIVES
Use this weather map to estimate the value
of the directional derivative of the temperature
function at Reno in
the southeasterly
direction.
Example 1
![Page 23: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/23.jpg)
DIRECTIONAL DERIVATIVES
The unit vector directed toward
the southeast is:
u = (i – j)/
However, we won’t need to use this expression.
Example 1
2
![Page 24: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/24.jpg)
DIRECTIONAL DERIVATIVES
We start by drawing a line through Reno
toward the southeast.
Example 1
![Page 25: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/25.jpg)
DIRECTIONAL DERIVATIVES
We approximate the directional derivative
DuT by:
The average rate of change of the temperature between the points where this line intersects the isothermals T = 50 and T = 60.
Example 1
![Page 26: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/26.jpg)
DIRECTIONAL DERIVATIVES
The temperature at the point southeast
of Reno is T = 60°F.
The temperature
at the point
northwest of Reno
is T = 50°F.
Example 1
![Page 27: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/27.jpg)
DIRECTIONAL DERIVATIVES
The distance between these points
looks to be about 75 miles.
Example 1
![Page 28: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/28.jpg)
DIRECTIONAL DERIVATIVES
So, the rate of change of the temperature
in the southeasterly direction is:
Example 1
60 50
7510
75
0.13 F/mi
D T
u
![Page 29: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/29.jpg)
DIRECTIONAL DERIVATIVES
When we compute the directional
derivative of a function defined by
a formula, we generally use the following
theorem.
![Page 30: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/30.jpg)
DIRECTIONAL DERIVATIVES
If f is a differentiable function of x and y,
then f has a directional derivative in
the direction of any unit vector u = <a, b>
and
Theorem 3
( , ) ( , ) ( , )x yD f x y f x y a f x y b u
![Page 31: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/31.jpg)
DIRECTIONAL DERIVATIVES
If we define a function g of the single
variable h by
then, by the definition of a derivative,
we have the following equation.
Proof
0 0( ) ( , ) g h f x ha y hb
![Page 32: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/32.jpg)
DIRECTIONAL DERIVATIVES
Proof—Equation 4
0
0 0 0 0
0
0 0
'(0)
( ) (0)lim
( , ) ( , )lim
( , )
h
h
g
g h g
hf x ha y hb f x y
h
D f x y
u
![Page 33: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/33.jpg)
DIRECTIONAL DERIVATIVES
On the other hand, we can write:
g(h) = f(x, y)
where: x = x0 + ha
y = y0 + hb
Proof
![Page 34: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/34.jpg)
DIRECTIONAL DERIVATIVES
Hence, the Chain Rule (Theorem 2
in Section 10.5) gives:
'( )
( , ) ( , )x y
f dx f dyg h
x dh y dh
f x y a f x y b
Proof
![Page 35: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/35.jpg)
DIRECTIONAL DERIVATIVES
If we now put h = 0,
then x = x0
y = y0
and
0 0 0 0'(0) ( , ) ( , ) x yg f x y a f x y b
Proof—Equation 5
![Page 36: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/36.jpg)
DIRECTIONAL DERIVATIVES
Comparing Equations 4 and 5,
we see that:
Proof
0 0
0 0 0 0
( , )
( , ) ( , )x y
D f x y
f x y a f x y b u
![Page 37: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/37.jpg)
DIRECTIONAL DERIVATIVES
Suppose the unit vector u makes
an angle θ with the positive x-axis, as
shown.
![Page 38: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/38.jpg)
DIRECTIONAL DERIVATIVES
Then, we can write
u = <cos θ, sin θ>
and the formula in Theorem 3
becomes:
Equation 6
( , ) ( , ) cos ( , )sinx yD f x y f x y f x y u
![Page 39: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/39.jpg)
DIRECTIONAL DERIVATIVES
Find the directional derivative Duf(x, y)
if: f(x, y) = x3 – 3xy + 4y2
u is the unit vector given by angle θ = π/6
What is Duf(1, 2)?
Example 2
![Page 40: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/40.jpg)
DIRECTIONAL DERIVATIVES
Formula 6 gives:
Example 2
2 12
212
( , ) ( , ) cos ( , )sin6 6
3(3 3 ) ( 3 8 )
2
3 3 3 8 3 3
x yD f x y f x y f x y
x y x y
x x y
u
![Page 41: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/41.jpg)
DIRECTIONAL DERIVATIVES
Therefore,
Example 2
212(1,2) 3 3(1) 3(1) 8 3 3 (2)
13 3 3
2
D fu
![Page 42: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/42.jpg)
DIRECTIONAL DERIVATIVES
The directional derivative Du f(1, 2)
in Example 2 represents the rate of
change of z in the direction of u.
![Page 43: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/43.jpg)
DIRECTIONAL DERIVATIVES
This is the slope of the tangent line to
the curve of intersection of the surface
z = x3 – 3xy + 4y2
and the vertical
plane through
(1, 2, 0) in the
direction of u
shown here.
![Page 44: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/44.jpg)
THE GRADIENT VECTOR
Notice from Theorem 3 that the directional
derivative can be written as the dot product
of two vectors:
( , ) ( , ) ( , )
( , ), ( , ) ,
( , ), ( , )
x y
x y
x y
D f x y f x y a f x y b
f x y f x y a b
f x y f x y
u
u
Expression 7
![Page 45: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/45.jpg)
THE GRADIENT VECTOR
The first vector in that dot product
occurs not only in computing directional
derivatives but in many other contexts
as well.
![Page 46: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/46.jpg)
THE GRADIENT VECTOR
So, we give it a special name:
The gradient of f
We give it a special notation too:
grad f or f , which is read “del f ”
![Page 47: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/47.jpg)
THE GRADIENT VECTOR
If f is a function of two variables x and y,
then the gradient of f is the vector function f
defined by:
Definition 8
( , ) ( , ), ( , )x yf x y f x y f x y
f f
x x
i j
![Page 48: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/48.jpg)
THE GRADIENT VECTOR
If f(x, y) = sin x + exy,
then
Example 3
( , ) ,
cos ,
(0,1) 2,0
x y
xy xy
f x y f f
x ye xe
f
![Page 49: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/49.jpg)
THE GRADIENT VECTOR
With this notation for the gradient vector, we
can rewrite Expression 7 for the directional
derivative as:
This expresses the directional derivative in the direction of u as the scalar projection of the gradient vector onto u.
Equation 9
( , ) ( , )D f x y f x y u u
![Page 50: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/50.jpg)
THE GRADIENT VECTOR
Find the directional derivative of the function
f(x, y) = x2y3 – 4y
at the point (2, –1) in the direction
of the vector v = 2 i + 5 j.
Example 4
![Page 51: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/51.jpg)
THE GRADIENT VECTOR
We first compute the gradient vector
at (2, –1):
Example 4
3 2 2( , ) 2 (3 4)
(2, 1) 4 8
f x y xy x y
f
i j
i j
![Page 52: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/52.jpg)
THE GRADIENT VECTOR
Note that v is not a unit vector.
However, since , the unit vector
in the direction of v is:
Example 4
| | 29v
2 5
| | 29 29 v
u i jv
![Page 53: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/53.jpg)
THE GRADIENT VECTOR
Therefore, by Equation 9,
we have:
Example 4
(2, 1) (2, 1)
2 5( 4 8 )
29 29
4 2 8 5 32
29 29
D f f
u u
i j i j
![Page 54: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/54.jpg)
FUNCTIONS OF THREE VARIABLES
For functions of three variables, we can
define directional derivatives in a similar
manner.
Again, Du f(x, y, z) can be interpreted as the rate of change of the function in the direction of a unit vector u.
![Page 55: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/55.jpg)
The directional derivative of f at (x0, y0, z0)
in the direction of a unit vector u = <a, b, c>
is:
if this limit exists.
THREE-VARIABLE FUNCTION Definition 10
0 0 0
0 0 0 0 0 0
0
( , , )
( , , ) ( , , )limh
D f x y z
f x ha y hb z hc f x y z
h
u
![Page 56: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/56.jpg)
If we use vector notation, then we can
write both Definitions 2 and 10 of the
directional derivative in a compact form,
as follows.
THREE-VARIABLE FUNCTIONS
![Page 57: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/57.jpg)
THREE-VARIABLE FUNCTIONS Equation 11
0 00
0
( ) ( )( ) lim
h
f h fD f
h
u
x u xx
where: x0 = <x0, y0> if n = 2
x0 = <x0, y0, z0> if n = 3
![Page 58: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/58.jpg)
This is reasonable.
The vector equation of the line through x0 in the direction of the vector u is given by x = x0 + t u (Equation 1 in Section 12.5).
Thus, f(x0 + hu) represents the value of f at a point on this line.
THREE-VARIABLE FUNCTIONS
![Page 59: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/59.jpg)
If f(x, y, z) is differentiable and u = <a, b, c>,
then the same method that was used to
prove Theorem 3 can be used to show
that:
THREE-VARIABLE FUNCTIONS Formula 12
( , , )
( , , ) ( , , ) ( , , )x y z
D f x y z
f x y z a f x y z b f x y z c u
![Page 60: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/60.jpg)
THREE-VARIABLE FUNCTIONS
For a function f of three variables,
the gradient vector, denoted by or grad f,
is:
f
( , , )
( , , ), ( , , , ), ( , , )x y z
f x y z
f x y z f x y z f x y z
![Page 61: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/61.jpg)
THREE-VARIABLE FUNCTIONS
For short,
, ,x y zf f f f
f f f
x y z
i j k
Equation 13
![Page 62: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/62.jpg)
THREE-VARIABLE FUNCTIONS
Then, just as with functions of two variables,
Formula 12 for the directional derivative can
be rewritten as:
( , , ) ( , , )D f x y z f x y z u u
Equation 14
![Page 63: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/63.jpg)
THREE-VARIABLE FUNCTIONS
If f(x, y, z) = x sin yz, find:
a. The gradient of f
b. The directional derivative of f at (1, 3, 0)
in the direction of v = i + 2 j – k.
Example 5
![Page 64: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/64.jpg)
THREE-VARIABLE FUNCTIONS
The gradient of f is:
Example 5 a
f (x, y, z)
fx(x, y, z), f
y(x, y, z), f
z(x, y, z)
sin yz,xz cos yz,xycos yz
![Page 65: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/65.jpg)
THREE-VARIABLE FUNCTIONS
At (1, 3, 0), we have:
The unit vector in the direction
of v = i + 2 j – k is:
Example 5 b
(1,3,0) 0,0,3f
1 2 1
6 6 6 u i j k
![Page 66: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/66.jpg)
THREE-VARIABLE FUNCTIONS
Hence, Equation 14 gives:
Example 5
(1,3,0) (1,3,0)
1 2 13
6 6 6
1 33
26
D f f
u u
k i j k
![Page 67: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/67.jpg)
MAXIMIZING THE DIRECTIONAL DERIVATIVE
Suppose we have a function f of two or three
variables and we consider all possible
directional derivatives of f at a given point.
These give the rates of change of f in all possible directions.
![Page 68: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/68.jpg)
MAXIMIZING THE DIRECTIONAL DERIVATIVE
We can then ask the questions:
In which of these directions does f change fastest?
What is the maximum rate of change?
![Page 69: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/69.jpg)
The answers are provided by
the following theorem.
MAXIMIZING THE DIRECTIONAL DERIVATIVE
![Page 70: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/70.jpg)
Suppose f is a differentiable function of
two or three variables.
The maximum value of the directional
derivative Duf(x) is:
It occurs when u has the same direction as the gradient vector
MAXIMIZING DIRECTIONAL DERIV. Theorem 15
| ( ) |f x
( )f x
![Page 71: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/71.jpg)
From Equation 9 or 14, we have:
where θ is the angle
between and u.
MAXIMIZING DIRECTIONAL DERIV. Proof
| || | cos
| | cos
D f f f
f
u u u
f
![Page 72: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/72.jpg)
The maximum value of cos θ is 1.
This occurs when θ = 0.
So, the maximum value of Du f is:
It occurs when θ = 0, that is, when u has the same direction as .
MAXIMIZING DIRECTIONAL DERIV. Proof
| |f
f
![Page 73: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/73.jpg)
a. If f(x, y) = xey, find the rate of change
of f at the point P(2, 0) in the direction
from P to Q(½, 2).
MAXIMIZING DIRECTIONAL DERIV. Example 6
![Page 74: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/74.jpg)
b. In what direction does f have
the maximum rate of change?
What is this maximum rate of change?
MAXIMIZING DIRECTIONAL DERIV. Example 6
![Page 75: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/75.jpg)
We first compute the gradient vector:
MAXIMIZING DIRECTIONAL DERIV. Example 6 a
( , ) ,
,
(2,0) 1,2
x y
y y
f x y f f
e xe
f
![Page 76: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/76.jpg)
The unit vector in the direction of
is .
So, the rate of change of f in the direction
from P to Q is:
MAXIMIZING DIRECTIONAL DERIV. Example 6 a
1.5,2PQ ��������������
3 45 5, u
3 45 5
3 45 5
(2,0) (2,0)
1,2 ,
1( ) 2( ) 1
D f f u
u
![Page 77: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/77.jpg)
According to Theorem 15, f increases
fastest in the direction of the gradient
vector .
So, the maximum rate of change is:
MAXIMIZING DIRECTIONAL DERIV. Example 6 b
(2,0) 1,2f
(2,0) 1,2 5f
![Page 78: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/78.jpg)
Suppose that the temperature at a point
(x, y, z) in space is given by
T(x, y, z) = 80/(1 + x2 + 2y2 + 3z2)
where: T is measured in degrees Celsius. x, y, z is measured in meters.
MAXIMIZING DIRECTIONAL DERIV. Example 7
![Page 79: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/79.jpg)
In which direction does the temperature
increase fastest at the point (1, 1, –2)?
What is the maximum rate of increase?
MAXIMIZING DIRECTIONAL DERIV. Example 7
![Page 80: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/80.jpg)
MAXIMIZING DIRECTIONAL DERIV.
The gradient of T is:
Example 7
2 2 2 2 2 2 2 2
2 2 2 2
2 2 2 2
160 320
(1 2 3 ) (1 2 3 )
480
(1 2 3 )
160( 2 3 )
(1 2 3 )
T T TT
x y z
x y
x y z x y z
z
x y z
x y zx y z
i j k
i j
k
i j k
![Page 81: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/81.jpg)
MAXIMIZING DIRECTIONAL DERIV.
At the point (1, 1, –2), the gradient vector
is:
Example 7
160256
58
(1,1, 2) ( 2 6 )
( 2 6 )
T
i j k
i j k
![Page 82: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/82.jpg)
MAXIMIZING DIRECTIONAL DERIV.
By Theorem 15, the temperature increases
fastest in the direction of the gradient vector
Equivalently, it does so in the direction of –i – 2 j + 6 k or the unit vector (–i – 2 j + 6 k)/ .
Example 7
58(1,1, 2) ( 2 6 )T i j k
41
![Page 83: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/83.jpg)
MAXIMIZING DIRECTIONAL DERIV.
The maximum rate of increase is the length
of the gradient vector:
Thus, the maximum rate of increase of temperature is:
Example 7
58
58
(1,1, 2) 2 6
41
T
i j k
58 41 4 C/m
![Page 84: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/84.jpg)
TANGENT PLANES TO LEVEL SURFACES
Suppose S is a surface with
equation
F(x, y, z)
That is, it is a level surface of a function F of three variables.
![Page 85: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/85.jpg)
TANGENT PLANES TO LEVEL SURFACES
Then, let
P(x0, y0, z0)
be a point on S.
![Page 86: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/86.jpg)
Then, let C be any curve that lies on
the surface S and passes through
the point P.
Recall from Section 10.1 that the curve C is described by a continuous vector function
r(t) = <x(t), y(t), z(t)>
TANGENT PLANES TO LEVEL SURFACES
![Page 87: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/87.jpg)
Let t0 be the parameter value
corresponding to P.
That is, r(t0) = <x0, y0, z0>
TANGENT PLANES TO LEVEL SURFACES
![Page 88: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/88.jpg)
Since C lies on S, any point (x(t), y(t), z(t))
must satisfy the equation of S.
That is,
F(x(t), y(t), z(t)) = k
Equation 16TANGENT PLANES
![Page 89: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/89.jpg)
If x, y, and z are differentiable functions of t
and F is also differentiable, then we can use
the Chain Rule to differentiate both sides of
Equation 16:
TANGENT PLANES
0F dx F dy F dz
x dt y dt x dt
Equation 17
![Page 90: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/90.jpg)
However, as
and
Equation 17 can be written in terms
of a dot product as:
TANGENT PLANES
'( ) 0F t r
, ,x y zF F F F
'( ) '( ), '( ), '( )t x t y t z t r
![Page 91: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/91.jpg)
TANGENT PLANES
In particular, when t = t0,
we have:
r(t0) = <x0, y0, z0>
So, 0 0 0 0( , , ) '( ) 0F x y z t r
Equation 18
![Page 92: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/92.jpg)
TANGENT PLANES
Equation 18 says:
The gradient vector at P, , is perpendicular to the tangent vector r’(t0) to any curve C on S that passes through P.
0 0 0( , , )F x y z
![Page 93: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/93.jpg)
TANGENT PLANES
If , it is thus natural to
define the tangent plane to the level surface
F(x, y, z) = k at P(x0, y0, z0) as:
The plane that passes through P and has normal vector
0 0 0( , , ) 0F x y z
0 0 0( , , )F x y z
![Page 94: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/94.jpg)
TANGENT PLANES
Using the standard equation of a plane
(Equation 7 in Section 12.5), we can write
the equation of this tangent plane as:
0 0 0 0 0 0 0 0
0 0 0 0
( , , )( ) ( , , )( )
( , , )( ) 0
x y
z
F x y z x x F x y z y y
F x y z z z
Equation 19
![Page 95: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/95.jpg)
NORMAL LINE
The normal line to S at P is
the line:
Passing through P
Perpendicular to the tangent plane
![Page 96: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/96.jpg)
TANGENT PLANES
Thus, the direction of the normal line
is given by the gradient vector
0 0 0( , , )F x y z
![Page 97: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/97.jpg)
TANGENT PLANES
So, by Equation 3 in Section 12.5,
its symmetric equations are:
Equation 20
0 0 0
0 0 0 0 0 0 0 0 0( , , ) ( , , ) ( , , )x y z
x x y y z z
F x y z F x y z F x y z
![Page 98: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/98.jpg)
TANGENT PLANES
Consider the special case in which
the equation of a surface S is of the form
z = f(x, y)
That is, S is the graph of a function f of two variables.
![Page 99: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/99.jpg)
TANGENT PLANES
Then, we can rewrite the equation as
F(x, y, z) = f(x, y) – z = 0
and regard S as a level surface
(with k = 0) of F.
![Page 100: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/100.jpg)
TANGENT PLANES
Then,
0 0 0 0 0
0 0 0 0 0
0 0 0
( , , ) ( , )
( , , ) ( , )
( , , ) 1
x x
y y
z
F x y z f x y
F x y z f x y
F x y z
![Page 101: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/101.jpg)
TANGENT PLANES
So, Equation 19 becomes:
This is equivalent to Equation 2 in Section 10.4
0 0 0 0 0 0
0
( , )( ) ( , )( )
( ) 0
x yf x y x x f x y y y
z z
![Page 102: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/102.jpg)
TANGENT PLANES
Thus, our new, more general, definition
of a tangent plane is consistent with
the definition that was given for the special
case of Section 10.4
![Page 103: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/103.jpg)
TANGENT PLANES
Find the equations of the tangent plane
and normal line at the point (–2, 1, –3)
to the ellipsoid
Example 8
2 22 3
4 9
x zy
![Page 104: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/104.jpg)
TANGENT PLANES
The ellipsoid is the level surface
(with k = 3) of the function
Example 8
2 22( , , )
4 9
x zF x y z y
![Page 105: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/105.jpg)
TANGENT PLANES
So, we have:
Example 8
23
( , , )2
( , , ) 2
2( , , )
9
( 2,1, 3) 1
( 2,1, 3) 2
( 2,1, 3)
x
y
z
x
y
z
xF x y z
F x y z y
zF x y z
F
F
F
![Page 106: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/106.jpg)
TANGENT PLANES
Then, Equation 19 gives the equation
of the tangent plane at (–2, 1, –3)
as:
This simplifies to:
3x – 6y + 2z + 18 = 0
Example 8
231( 2) 2( 1) ( 3) 0x y z
![Page 107: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/107.jpg)
TANGENT PLANES
By Equation 20, symmetric equations
of the normal line are:
23
2 1 3
1 2
x y z
Example 8
![Page 108: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/108.jpg)
TANGENT PLANES
The figure shows
the ellipsoid,
tangent plane,
and normal line
in Example 8.
Example 8
![Page 109: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/109.jpg)
SIGNIFICANCE OF GRADIENT VECTOR
We now summarize the ways
in which the gradient vector is
significant.
![Page 110: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/110.jpg)
We first consider a function f of
three variables and a point P(x0, y0, z0)
in its domain.
SIGNIFICANCE OF GRADIENT VECTOR
![Page 111: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/111.jpg)
On the one hand, we know from Theorem 15
that the gradient vector gives
the direction of fastest increase of f.
SIGNIFICANCE OF GRADIENT VECTOR
0 0 0( , , )f x y z
![Page 112: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/112.jpg)
On the other hand, we know that
is orthogonal to the level
surface S of f through P.
SIGNIFICANCE OF GRADIENT VECTOR
0 0 0( , , )f x y z
![Page 113: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/113.jpg)
These two properties are quite
compatible intuitively.
As we move away from P on the level surface S, the value of f does not change at all.
SIGNIFICANCE OF GRADIENT VECTOR
![Page 114: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/114.jpg)
So, it seems reasonable that, if we
move in the perpendicular direction,
we get the maximum increase.
SIGNIFICANCE OF GRADIENT VECTOR
![Page 115: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/115.jpg)
In like manner, we consider a function f
of two variables and a point P(x0, y0)
in its domain.
SIGNIFICANCE OF GRADIENT VECTOR
![Page 116: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/116.jpg)
Again, the gradient vector
gives the direction of fastest increase
of f.
SIGNIFICANCE OF GRADIENT VECTOR
0 0( , )f x y
![Page 117: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/117.jpg)
Also, by considerations similar to our
discussion of tangent planes, it can be
shown that:
is perpendicular to the level curve f(x, y) = k that passes through P.
SIGNIFICANCE OF GRADIENT VECTOR
0 0( , )f x y
![Page 118: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/118.jpg)
Again, this is intuitively plausible.
The values of f remain constant as we move along the curve.
SIGNIFICANCE OF GRADIENT VECTOR
![Page 119: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/119.jpg)
Now, we consider a topographical map
of a hill.
Let f(x, y) represent the height above
sea level at a point with coordinates (x, y).
SIGNIFICANCE OF GRADIENT VECTOR
![Page 120: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/120.jpg)
Then, a curve of steepest ascent can be
drawn by making it perpendicular to all of
the contour lines.
SIGNIFICANCE OF GRADIENT VECTOR
![Page 121: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/121.jpg)
This phenomenon can also be noticed in
this figure in Section 10.1,
where Lonesome
Creek follows
a curve of steepest
descent.
SIGNIFICANCE OF GRADIENT VECTOR
![Page 122: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/122.jpg)
Computer algebra systems have commands
that plot sample gradient vectors.
Each gradient vector is plotted
starting at the point (a, b).
SIGNIFICANCE OF GRADIENT VECTOR
( , )f a b
![Page 123: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/123.jpg)
The figure shows such a plot—called
a gradient vector field—for the function
f(x, y) = x2 – y2
superimposed on
a contour map of f.
GRADIENT VECTOR FIELD
![Page 124: PARTIAL DERIVATIVES 11. PARTIAL DERIVATIVES 11.6 Directional Derivatives and the Gradient Vector In this section, we will learn how to find: The rate](https://reader035.vdocuments.us/reader035/viewer/2022062321/56649f1c5503460f94c31ffb/html5/thumbnails/124.jpg)
As expected,
the gradient vectors:
Point “uphill”
Are perpendicular to the level curves
SIGNIFICANCE OF GRADIENT VECTOR