

Are these the arrows of outrageous fortune of which we have heard?...
In measuring position by distance and direction we used a line
with an arrowhead on it. This representation of position is
called a "vector". Just as there is an arithmetic of
numbers, there is an arithmetic of vectors. We are going to be
interested in motion of particles and have already said that
motion is change in position over time. To get a change in a
number it is customary to subtract the initial value from the
final. To get a change in position we will use the same
technique, subtracting the starting position from the ending. So
how would you subtract two positions? Well it is most convenient
to think of the positions as vectors to do this.
We have identified a line segment with direction and magnitude
as a vector. If V is a particular vector, then
V symbolizes its magnitude. Quantities that can be
specified by a magnitude only are called "scalar"
quantities to distinguish them from vectors. Since scalar
quantities are just numbers, they may be added in the ordinary
way. An addition operation is also defined for vectors. Adding a
mixture of vectors and scalars is not defined.
To add vectors there are two techniques available, geometric
addition and algebraic addition. Both yield the same result. The choice of which technique
to use in adding vectors depends on the application and is a
matter of convenience. First we will discuss geometric vector
addition. Since a vector is defined by its magnitude and
direction, changing its location in our reference frame without
changing its direction or magnitude leaves it the same vector. We
are free to relocate a vector anywhere in our space where we find
it convenient. To add vectors geometrically we just place the
tail of one at the head of the other. The sum then is a vector
from the tail of the first vector to the head of the last. Run
the
Geometric Vector Addition
display to see how this works.



The algebraic addition of
vectors involves simply adding up the like components of the
vectors. Imagine a vector with its tail at the origin. The
"scalar components" of that vector are just the
coordinates of the head of the vector. Remember that the
coordinates were the distances along each axis which defined the
position of the head of the vector. To add two vectors, add all
the x components together and all the y components. The algebraic
addition of vectors works because the sums of the components are
the components of the sum. Run the
Algebraic Vector Addition display to see examples of
this.


To break a vector down into its components is called vector
resolution. What that means is that we will resolve a vector into
its "vector components". I have already defined the
scalar components of a vector as the coordinates of the head of
the vector when its tail is at the origin. The vector components
are the vectors lying along the axes that add up to the vector
we are interested in. Run the
Vector Resolution
display to see an example in 3 dimensions. Just
for variety we will use a 3 dimensional vector here. In general
we will use the fewest number of dimensions needed to convey the
desired information but I wanted to illustrate that vectors in 3
dimensions differ from 2 dimensional vectors only in that they
involve an extra coordinate.



Since a vector along an axis has its direction fixed by
definition, only its magnitude changes. That means we can
uniquely define each component of a vector with just a number,
knowing whether we are talking about the x, y or z component.
Remember that we could also specify the position of a particle
with the scalars that we called coordinates. There is an obvious
relationship between the coordinates of a position and the
components of the vector pointing to that position. They are
numerically equal.
Recognize that in vectors as in numbers, subtraction is just
addition with the sign of the entity to be subtracted reversed.
That of course brings up the question of what it means to have
the sign of a vector reversed. To reverse the sign of a number we
just multiply it by 1. To apply the same trick to a vector we
must agree on what it means to multiply a vector by a scalar, in
this case 1.
The product of a scalar times a vector is a vector whose
components are the components of the original vector, each
multiplied by the scalar. Except for some special cases which I
will cover soon, we will use bold letters to symbolize
vectors. Run the
Scalar Vector Multiplication
display to see how this multiplication works.
Verify that when the scalar multiplier is 1, the product vector
has the same magnitude as the original and points the other
way.


We got into this discussion of multiplying a vector times a
scalar by trying to find a way to subtract vectors. Another way
to look at this subtraction business is to define the negative of
a vector as that vector, which when added to the original yields
a null result. That means the sum is a vector of zero length.
Pretty clearly that is the case if the negative vector has the
same length as the original and points the other way.
One last view of vector
subtraction comes from our discussion of geometric vector
addition. Remember that the sum of two vectors is a vector from
the tail of the first to the head of the second. Now consider a
vector P which is the difference between two vectors,
P_{2} and P_{1}. This means
P=P_{2}P_{1}, or by
rearranging the terms,
P_{2}=P_{1}+P. So
P_{2} must run from the tail of
P_{1} to the head of P. That makes
P a vector from the head of P_{1} to the
head of P_{2}. To help visualize why this is so,
Run the Geometric Vector Subtraction
display. We will work in two dimensions since any three vectors
which form a closed figure must be "coplanar" meaning
all in one plane. (Think about it.)

Having added, subtracted, multiplied by a scalar and resolved
vectors, we may as well finish the story with the multiplication
of one vector by another. We will look at two ways to multiply
vectors, both of which have physical significance. First we will
consider the scalar product of two vectors. It is called the
scalar product because the result of the multiplication is a
scalar quantity. This is distinctly different from the scalar
multiplication that we already covered. Sorry about the use of
the word scalar in both cases. In fact that may be why an
alternate name for this kind of multiplication has come into use.
It is also called the "dot product" after the dot
symbol that distinguishes this kind of multiplication. The dot
product of two vectors V_{1} and
V_{2} is written V_{1} ·
V_{2} where the "·" between the
vectors is the scalar product operator.
To get the dot product of two vectors, multiply the two lengths
together and then multiply by the cosine of the angle between
them. This may be thought of as the product of the length of one
vector times the component of the other vector in the direction
of the first. Run the Dot Product
display to see some examples.


I
promised you two ways to multiply vectors together. I know this
seems excessive, two different ways to multiply objects together,
but we are going to need both ways when we come to the ideas of
work and torque.
The second process yields a vector so it is
called the vector product. It is also known as the "cross
product" due to the operation being symbolized by an X, as
in V = V_{1} X V_{2}
where V is the cross product of the vectors
V_{1} and V_{2}.
V_{1} X V_{2} is defined as a
vector whose length is the product of the lengths of
V_{1} and V_{2} times the sine of
the angle between them, and whose direction is perpendicular to
both V_{1} and V_{2}. The sense
of the cross product is determined by a right hand rule. The
right hand rule in this case works like this. Place the fingers
of your right hand in the direction of V_{1} and
curl them toward V_{2}. The cross product will
point to the side where your thumb is located. Run the Cross Product display for an illustration.
Notice that V_{1} X V_{2} =
(V_{2} X V_{1}).
As we did for the dot product, we can calculate the cross product
without knowing explicitly the angle between the vectors. First
look at the cross products of the unit vectors like this:
X
= X = X = 0 and
X = , X = , X = .
To help you remember what equals what in
the above relationships, notice that the order of the unit vectors
remains the same in each equation. follows
which follows if you
loop around to the beginning from the end of each equation.
Then suppose V_{1} = a * + b * + c * and
V_{2} =
d * + e * + f * .
Next multiply V_{1} X V_{2}
by multiplying each term in V_{1} by each term
in V_{2}.
V_{1} X
V_{2} =
a * d * X + a * e * X + a * f * X +
b * d * X + b * e * X + b * f * X +
c * d * X + c * e * X + c * f * X
Next replace all the cross products with their equivalent value.
V_{1} X
V_{2} =
a * d * 0 + a * e * + a
* f * ( ) +
b * d * ( ) + b * e * 0
+ b * f * +
c * d * + c * e *
( ) + c * f * 0
Now collect the coefficients of the unit vectors to find
V_{1} X
V_{2} = (b * f  c * e) * + (c * d  a * f) * + (a * e  b * d) *
For those of you who remember determinants from your high
school algebra days, you might recognize the formula we just
developed for the cross product as the expansion of a determinant
that looks like this:
V_{1} X V_{2} =
This is as far as we will go for now with vector arithmetic.
The next section of the course puts much of this vector
arithmetic into action.
Are there any questions?


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