Math 302 Lecture Notes Linear Algebra and Multivariable Calculus Kenneth L. Kuttler All Chapter Instant Download
Math 302 Lecture Notes Linear Algebra and Multivariable Calculus Kenneth L. Kuttler All Chapter Instant Download
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Math 302 Lecture Notes
Kenneth Kuttler
February 4, 2015
2
Contents
I Introduction 11
2 Vector Products 37
2.1 The Dot Product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.1.1 Definition In terms Of Coordinates . . . . . . . . . . . . . . . . . . . . 37
2.1.2 The Geometric Meaning Of The Dot Product, The Included Angle . . 38
2.1.3 The Cauchy Schwarz Inequality . . . . . . . . . . . . . . . . . . . . . . 40
2.1.4 The Triangle Inequality . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.1.5 Direction Cosines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.1.6 Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.1.7 Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.2 The Cross Product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.2.1 Cross Product, Geometric Description . . . . . . . . . . . . . . . . . . 47
2.2.2 The Coordinate Description Of The Cross Product . . . . . . . . . . . 49
2.2.3 A Physical Application . . . . . . . . . . . . . . . . . . . . . . . . . . 50
2.2.4 The Box Product, Triple Product . . . . . . . . . . . . . . . . . . . . 51
2.2.5 A Proof Of The Distributive Law For The Cross Product∗ . . . . . . . 52
2.2.6 Torque, Moment Of A Force∗ . . . . . . . . . . . . . . . . . . . . . . . 53
2.2.7 Angular Velocity∗ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
2.2.8 Center Of Mass∗ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
2.3 Further Explanations∗ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
2.3.1 The Distributive Law For The Cross Product∗ . . . . . . . . . . . . . 56
2.3.2 Vector Identities And Notation∗ . . . . . . . . . . . . . . . . . . . . . 58
2.4 Exercises With Answers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3
4 CONTENTS
5 Matrices 95
5.1 Matrix Operations And Algebra . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.1.1 Addition And Scalar Multiplication Of Matrices . . . . . . . . . . . . 95
5.1.2 Multiplication Of Matrices . . . . . . . . . . . . . . . . . . . . . . . . 98
5.1.3 The ij th Entry Of A Product . . . . . . . . . . . . . . . . . . . . . . . 101
5.1.4 Graphs And Digraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.1.5 Properties Of Matrix Multiplication . . . . . . . . . . . . . . . . . . . 105
5.1.6 The Transpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.1.7 The Identity And Inverses . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.2 Finding The Inverse Of A Matrix, Gauss Jordan Method . . . . . . . . . . . . 110
5.3 Systems Of Equations And Matrices . . . . . . . . . . . . . . . . . . . . . . . 116
5.4 Elementary Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.5 Finding Linear Relationships Between Vectors . . . . . . . . . . . . . . . . . . 124
5.5.1 The Great And Glorious Lemma On Row Operations . . . . . . . . . 125
5.5.2 Theory Of Row Reduced Echelon Form∗ . . . . . . . . . . . . . . . . . 127
5.6 Block Multiplication Of Matrices∗ . . . . . . . . . . . . . . . . . . . . . . . . 131
5.7 Exercises With Answers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
5.8 The Rank Of A Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
5.9 Exercises With Answers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
7 Determinants 153
7.1 Basic Techniques And Properties . . . . . . . . . . . . . . . . . . . . . . . . . 153
7.1.1 Cofactors And 2 × 2 Determinants . . . . . . . . . . . . . . . . . . . . 153
7.1.2 The Determinant Of A Triangular Matrix . . . . . . . . . . . . . . . . 156
7.1.3 Properties Of Determinants . . . . . . . . . . . . . . . . . . . . . . . . 158
7.1.4 Finding Determinants Using Row Operations . . . . . . . . . . . . . . 159
CONTENTS 5
V Differentiability 327
16 Differentiability 329
16.1 The Definition Of Differentiability . . . . . . . . . . . . . . . . . . . . . . . . 329
16.2 C 1 Functions And Differentiability . . . . . . . . . . . . . . . . . . . . . . . . 331
16.3 The Directional Derivative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333
16.3.1 Separable Differential Equations∗ . . . . . . . . . . . . . . . . . . . . . 334
16.4 Exercises With Answers∗ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337
16.4.1 A Heat Seaking Particle . . . . . . . . . . . . . . . . . . . . . . . . . . 338
16.5 The Chain Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338
16.5.1 Related Rates Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 341
CONTENTS 7
Introduction
11
13
These are the lecture notes for my section of Math 302. They are pretty much in
the order of the syllabus for the course. You don’t need to read the starred sections and
chapters and subsections. These are there to provide depth in the subject. To quote from
the mission statement of BYU, “ Depth comes when students realize the effect of rigorous,
coherent, and progressively more sophisticated study. Depth helps students distinguish
between what is fundamental and what is only peripheral; it requires focus, provides intense
concentration. ...” To see clearly what is peripheral you need to read the fundamental and
difficult concepts, most of which are presented in the starred sections. These are not always
easy to read and I have indicated the most difficult with a picture of a dragon. Some are
not much harder than what is presented in the course. A good example is the one which
defines the derivative. If you don’t learn this material, you will have trouble understanding
many fundamental topics. Some which come to mind are basic continuum mechanics (The
deformation gradient is a derivative.) and Newton’s method for solving nonlinear systems
of equations.(The entire method involves looking at the derivative and its inverse.) If you
don’t want to learn anything more than what you will be tested on, then you can omit these
sections. This is up to you. It is your choice.
A word about notation might help. Most of the linear algebra works in any field. Exam-
ples are the rational numbers, the integers modulo a prime number, the complex numbers,
or the real numbers. Therefore, I will often write F to denote this field. If you don’t like
this, just put in R and you will be fine. This is the main one of interest. However, I at least
want you to realize that everything holds for the complex numbers in addition to the reals.
In many applications this is essential so it does not hurt to begin to realize this. Also, I will
write vectors in terms of bold letters. Thus u will denote a vector. Sometimes people write
something like ⃗u to indicate a vector. However, the bold face is the usual notation so I am
using this in these notes. On the board, I will likely write the other notation. The norm
or length of a vector is often written as ||u|| . I will usually write it as |u| . This is standard
notation also although most books use the double bar notation. The notation I am using
emphasizes that the norm is just like the absolute value which is an important connection
to make. It also seems less cluttered. You need to understand that either notation means
the same thing.
For a more substantial treatment of certain topics, there is a complete calculus book on
my web page. There are significant generalizations which unify all the notions of volume
into one beautiful theory. I have not pursued this topic in these notes but it is in the calculus
book. There are other things also, especially all the one variable theory if you need a review.
14
Part II
15
Chapter 1
Rn ≡ {(x1 , · · ·, xn ) : xj ∈ R for j = 1, · · ·, n} .
{(0, · · ·, 0, t, 0, · · ·, 0) : t ∈ R }
for t in the ith slot is called the ith coordinate axis coordinate axis, the xi axis for short.
The point 0 ≡ (0, · · ·, 0) is called the origin.
Thus (1, 2, 4) ∈ R3 and (2, 1, 4) ∈ R3 but (1, 2, 4) ̸= (2, 1, 4) because, even though the
same numbers are involved, they don’t match up. In particular, the first entries are not
equal.
Why would anyone be interested in such a thing? First consider the case when n = 1.
Then from the definition, R1 = R. Recall that R is identified with the points of a line.
Look at the number line again. Observe that this amounts to identifying a point on this
line with a real number. In other words a real number determines where you are on this
line. Now suppose n = 2 and consider two lines which intersect each other at right angles
as shown in the following picture.
6 · (2, 6)
(−8, 3) · 3
2
−8
17
18 CHAPTER 1. VECTORS AND POINTS IN RN
Notice how you can identify a point shown in the plane with the ordered pair, (2, 6) .
You go to the right a distance of 2 and then up a distance of 6. Similarly, you can identify
another point in the plane with the ordered pair (−8, 3) . Go to the left a distance of 8 and
then up a distance of 3. The reason you go to the left is that there is a − sign on the eight.
From this reasoning, every ordered pair determines a unique point in the plane. Conversely,
taking a point in the plane, you could draw two lines through the point, one vertical and the
other horizontal and determine unique points, x1 on the horizontal line in the above picture
and x2 on the vertical line in the above picture, such that the point of interest is identified
with the ordered pair, (x1 , x2 ) . In short, points in the plane can be identified with ordered
pairs similar to the way that points on the real line are identified with real numbers. Now
suppose n = 3. As just explained, the first two coordinates determine a point in a plane.
Letting the third component determine how far up or down you go, depending on whether
this number is positive or negative, this determines a point in space. Thus, (1, 4, −5) would
mean to determine the point in the plane that goes with (1, 4) and then to go below this
plane a distance of 5 to obtain a unique point in space. You see that the ordered triples
correspond to points in space just as the ordered pairs correspond to points in a plane and
single real numbers correspond to points on a line.
You can’t stop here and say that you are only interested in n ≤ 3. What if you were
interested in the motion of two objects? You would need three coordinates to describe
where the first object is and you would need another three coordinates to describe where
the other object is located. Therefore, you would need to be considering R6 . If the two
objects moved around, you would need a time coordinate as well. As another example,
consider a hot object which is cooling and suppose you want the temperature of this object.
How many coordinates would be needed? You would need one for the temperature, three
for the position of the point in the object and one more for the time. Thus you would need
to be considering R5 . Many other examples can be given. Sometimes n is very large. This
is often the case in applications to business when they are trying to maximize profit subject
to constraints. It also occurs in numerical analysis when people try to solve hard problems
on a computer.
There are other ways to identify points in space with three numbers but the one presented
is the most basic. In this case, the coordinates are known as Cartesian coordinates after
Descartes1 who invented this idea in the first half of the seventeenth century. I will often
not bother to draw a distinction between the point in n dimensional space and its Cartesian
coordinates.
x + y = (x1 , · · ·, xn ) + (y1 , · · ·, yn )
≡ (x1 + y1 , · · ·, xn + yn ) (1.2)
1 René Descartes 1596-1650 is often credited with inventing analytic geometry although it seems the ideas
were actually known much earlier. He was interested in many different subjects, physiology, chemistry, and
physics being some of them. He also wrote a large book in which he tried to explain the book of Genesis
scientifically. Descartes ended up dying in Sweden.
1.3. GEOMETRIC MEANING OF VECTORS 19
With this definition, the algebraic properties satisfy the conclusions of the following
theorem.
Theorem 1.2.2 For v, w vectors in Rn and α, β scalars, (real numbers), the fol-
lowing hold.
v + w = w + v, (1.3)
the commutative law of addition,
(v + w) + z = v+ (w + z) , (1.4)
v+ (−v) = 0, (1.6)
α (v + w) = αv+αw, (1.7)
(α + β) v =αv+βv, (1.8)
α (βv) = αβ (v) , (1.9)
1v = v. (1.10)
In the above 0 = (0, · · ·, 0).
You should verify these properties all hold. For example, consider 1.7
α (v + w) = α (v1 + w1 , · · ·, vn + wn )
= (α (v1 + w1 ) , · · ·, α (vn + wn ))
= (αv1 + αw1 , · · ·, αvn + αwn )
= (αv1 , · · ·, αvn ) + (αw1 , · · ·, αwn )
= αv + αw.
(x1 , x2 , x3 ) = x
3
20 CHAPTER 1. VECTORS AND POINTS IN RN
Then this arrow is called the position vector of the point, x. Given two points, P, Q
whose coordinates are (p1 , · · ·, pn ) and (q1 , · · ·, qn ) respectively, one can also determine the
vector defined as follows. Also one can obtain a vector from a given two points.
−−→
P Q ≡ (q1 − p1 , · · ·, qn − pn )
Thus every point determines such a vector and conversely, every such vector (arrow)
which has its tail at 0 determines a point of Rn , namely the point of Rn which coincides
with the point of the vector.
Imagine taking the above position vector and moving it around, always keeping it point-
ing in the same direction as shown in the following picture.
3 (x1 , x2 , x3 ) = x
3
3 3
After moving it around, it is regarded as the same vector because it points in the same
direction and has the same length.2 Thus each of the arrows in the above picture is regarded
as the same vector. The components of this vector are the numbers, x1 , · · ·, xn . You
should think of these numbers as directions for obtainng an arrow. Starting at some point,
(a1 , a2 , · · ·, an ) in Rn , you move to the point (a1 + x1 , · · ·, an ) and from there to the point
(a1 + x1 , a2 + x2 , a3 · ··, an ) and then to (a1 + x1 , a2 + x2 , a3 + x3 , · · ·, an ) and continue this
way until you obtain the point (a1 + x1 , a2 + x2 , · · ·, an + xn ) . The arrow having its tail
at (a1 , a2 , · · ·, an ) and its point at (a1 + x1 , a2 + x2 , · · ·, an + xn ) looks just like the arrow
which has its tail at 0 and its point at (x1 , · · ·, xn ) so it is regarded as the same vector.
be defined in such a way that it does not change when such motion takes place.
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