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Salas, Hille, Etgen Calculus: One and Several Variables Copyright 2007 © John Wiley & Sons, Inc. All rights reserved.
Section 16.1 Differentiability and Gradient
a. Definition: Differentiability
b. Definition: Gradient
c. Theorem 16.1.3
d. Differentiability Implies Continuity
Section 16.2 Gradients and Directional Derivatives
a. Elementary Formulas
b. Directional Derivative
c. Theorem 16.2.4
d. Theorem 16.2.5
e. Rate of Change
Section 16.3 The Mean-Value Theorem: The Chain Rule
a. The Mean-Value Theorem
b. Connected Sets
c. Theorem 16.3.2
d. Theorem 16.3.3
e. The Chain Rule
f. Another Formulation of the Chain Rule
g. Implicit Differentiation
Section 16.4 The Gradient as a Normal; Tangent Lines and Tangent Planes
a. Gradient Vectors
b. Normal and Tangent Vectors
c. Tangent and Normal Lines
d. Functions of Three Variables
e. Tangent Planes
f. Equations for the Normal Line
g. Surface z = g(x,y) and the Normal Line
Chapter 16: Gradients; Extreme Values; Differentials
Section 16.5 Local Extreme Values
a. Definition
b. Theorem 16.5.2
c. Critical Points, Stationary Points, Saddle Points
d. Second-Partials Test
Section 16.6 Absolute Extreme Values
a. Definition: Absolute Extreme Values
b. Definition: Bounded Sets
c. Extreme-Value Theorem
d. Two Variables: Absolute Extreme Values
Section 16.7 Maxima and Minima with Side Conditions
a. Lagrange Multiplier
b. Illustration
Section 16.8 Differentials
a. Illustration of a Differential
b. Differentials and Increments
c. Illustration
Section 16.9 Reconstructing a Function from its Gradient
a. Reconstruction: Parts 1, 2 and 3
b. Simply Connected
c. Theorem 16.9.2
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Salas, Hille, Etgen Calculus: One and Several Variables Copyright 2007 © John Wiley & Sons, Inc. All rights reserved.
Differentiability and Gradient
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Salas, Hille, Etgen Calculus: One and Several Variables Copyright 2007 © John Wiley & Sons, Inc. All rights reserved.
Differentiability and Gradient
Example
For the function f (x, y) = x 2 + y 2 .
The remainder g(h) = ||h|| 2 is o(h):
as h → 0,
( ) ( ) ( ) ( )
( ) ( )
[ ]
[ ]
1 2
2 2 2 2
1 2
2 2
1 2 1 2
2
, ,
2 2
2 2
f f f x h y h f x y
x h y h x y
xh yh h h
x y
+ − = + + −
= + + + − +
= + + +
= + ⋅ +
x h x
i j h h
2
= →
h h 0
h thus ∇ f ( ) x = ∇ f x y ( ) , = 2 x i + 2 y j
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Salas, Hille, Etgen Calculus: One and Several Variables Copyright 2007 © John Wiley & Sons, Inc. All rights reserved.
Differentiability and Gradient
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Salas, Hille, Etgen Calculus: One and Several Variables Copyright 2007 © John Wiley & Sons, Inc. All rights reserved.
Differentiability and Gradient
Differentiability Implies Continuity
As in the one-variable case, differentiability implies continuity:
To see this, write
( ) ( ) ( ) ( )
f x + h − f x = ∇ f x h ⋅ + o h and note that
( ) ( ) ( ) ( ) ( ) ( )
f x + h − f x = ∇ f x h ⋅ + o h ≤ ∇ f x h ⋅ + o h As h → 0,
It follows that
f (x + h ) − f (x) → 0 and therefore f (x + h) → f (x).
( ) ( ) 0 and ( ) 0
f f o
∇ x h ⋅ ≤ ∇ x h → h →
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Salas, Hille, Etgen Calculus: One and Several Variables Copyright 2007 © John Wiley & Sons, Inc. All rights reserved.
Some Elementary Formulas
In many respects gradients behave just as derivatives do in the one-variable case. In particular, if f (x) and g(x) exist, then [ f (x) + g(x)], [ αf (x)], and [ f (x)g(x)]
all exist, and
Gradients and Directional Derivatives
∇ ∇ ∇
∇ ∇
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Gradients and Directional Derivatives
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Gradients and Directional Derivatives
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Gradients and Directional Derivatives
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Gradients and Directional Derivatives
Since the directional derivative gives the rate of change of the function in
that direction, it is clear that
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The Mean-Value Theorem: The Chain Rule
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The Mean-Value Theorem: The Chain Rule
A nonempty open set U (in the plane or in three-space) is said to be connected if
any two points of U can be joined by a polygonal path that lies entirely in U. You
can see such a set pictured in Figure 16.3.1. The set shown in Figure 16.3.2 is
the union of two disjoint open sets. The set is open but not connected: it is
impossible to join a and b by a polygonal path that lies within the set.
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The Mean-Value Theorem: The Chain Rule
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The Mean-Value Theorem: The Chain Rule
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The Mean-Value Theorem: The Chain Rule
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The Mean-Value Theorem: The Chain Rule
Another Formulation of Theorem 16.3.4 The chain rule for functions of one variable, gives
In the two-variable case, the z-term drops out and we have
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The Mean-Value Theorem: The Chain Rule
Implicit Differentiation
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The Gradient as a Normal
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The Gradient as a Normal
Consider now a curve in the xy-plane
C : f (x, y) = c.
We can view C as the c-level curve of f and conclude from (16.4.1) that the gradient
is perpendicular to C at (x 0 , y 0 ). We call it a normal vector. The vector
is perpendicular to the gradient:
It is therefore a tangent vector.
( 0 , 0 ) ( 0 , 0 ) f ( 0 , 0 ) ( f 0 , 0 ) f ( 0 , 0 ) ( f 0 , 0 ) 0
f x y t x y x y x y x y x y
x y y x
∂ ∂ ∂ ∂
∇ ⋅ = − =
∂ ∂ ∂ ∂
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The Gradient as a Normal
Tangent Line
Normal Line
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The Gradient as a Normal
Functions of Three Variables
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The Gradient as a Normal
A point x lies on the tangent plane through x 0 iff
This is an equation for the tangent plane in vector notation. In Cartesian
coordinates the equation takes the form
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The normal line to the surface f (x, y, z) = c at a point x 0 = (x 0 , y 0 , z 0 ) on the surface is the line which passes through (x 0 , y 0 , z 0 ) parallel to f (x 0 ). Thus, f (x 0 ) is a direction vector for the normal line and
The Gradient as a Normal
∇ ∇
is a vector equation for the line. In scalar parametric form, equations for the
normal line can be written
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Salas, Hille, Etgen Calculus: One and Several Variables Copyright 2007 © John Wiley & Sons, Inc. All rights reserved.
The Gradient as a Normal
A surface of the form z = g(x, y) can be written in the form f (x, y, z) = 0
by setting
f (x, y, z) = g(x, y ) − z.
If g is differentiable, so is f .
If g(x 0 , y 0 ) = 0, then both partials of g are zero at (x 0 , y 0 ) and the equation reduces to
∇
In this case the tangent plane is horizontal.
Scalar parametric equations for the line normal to the surface z = g(x, y) at the
point (x 0 , y 0 , z 0 ) can be written
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Local Extreme Values
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Salas, Hille, Etgen Calculus: One and Several Variables Copyright 2007 © John Wiley & Sons, Inc. All rights reserved.
Local Extreme Values
In the one-variable case we know that if f has local extreme value at x 0 , then f ´ (x 0 ) = 0 or f ´ (x 0 ) does not exist.
We have a similar result for functions of several variables.
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Local Extreme Values
Two Variables
We suppose for the moment that f = f (x, y) is defined on an open connected set and is continuously differentiable there. The graph of f is a surface z = f (x, y). Where f has a local maximum, the surface has a local high point. Where f has a local
minimum, the surface has a local low point. Where f has either a local maximum or a local minimum, the gradient is 0 and therefore the tangent plane is horizontal.
Critical points at which the gradient is zero are called stationary points. The
stationary points that do not give rise to local extreme values are called saddle points.
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Local Extreme Values
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Absolute Extreme Values
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Absolute Extreme Values
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Absolute Extreme Values
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Absolute Extreme Values
Two Variables
The procedure we use for finding the absolute extreme values can be outlined as follows:
1. Determine the critical points. These are the interior points at which the gradient is zero (the stationary points) and the interior points at which the gradient does not exist.
2. Determine the points on the boundary that can possibly give rise to extreme values. At this stage this is a one-variable process.
3. Evaluate f at the points found in Steps 1 and 2.
4. The greatest of the numbers found in Step 3 is the absolute maximum; the
least is the absolute minimum.
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Maxima and Minima with Side Conditions
The Method of Lagrange
Let f be a function of two or three variables which is continuously differentiable on some open set U. We take
C : r = r (t), t ∈ I
to be a curve that lies entirely in U and has at each point a nonzero tangent vector r'(t).
Such a scalar λ has come to be called a Lagrange multiplier.
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Maxima and Minima with Side Conditions
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Differentials
We begin by reviewing the one-variable case. If f is differentiable at x, then for small h, the increment
Δf = f (x + h) − f (x) can be approximated by the differential
d f = f ´ (x) h.
For a geometric view of Δf and df, see Figure 16.8.1. We write f df
∆ ≅
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Salas, Hille, Etgen Calculus: One and Several Variables Copyright 2007 © John Wiley & Sons, Inc. All rights reserved.
Differentials
is called the increment of f , and the dot product
is called the differential (more formally, the total differential). As in the
one-variable case, for small h, the differential and the increment are
approximately equal:
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Differentials
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Reconstructing a Function from its Gradient
Part 1 Show how to find f (x, y) given its gradient
Part 2 Show that, although all gradients f (x, y) are expressions of the form
(set P = ∂ f/∂x and Q = ∂ f/∂y), not all such expressions are gradients.
Part 3 Recognize which expressions P(x, y) i + Q(x, y) j are actually gradients.
∇
( ) , f ( ) , f ( ) ,
f x y x y x y
x y
∂ ∂
∇ = +
∂ i ∂ j
P(x, y) i + Q(x, y) j
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Salas, Hille, Etgen Calculus: One and Several Variables Copyright 2007 © John Wiley & Sons, Inc. All rights reserved.
Reconstructing a Function from its Gradient
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Salas, Hille, Etgen Calculus: One and Several Variables Copyright 2007 © John Wiley & Sons, Inc. All rights reserved.