In most mathematics courses up until this point, we deal with scalars. These are quantities which only need one number to express. For instance, the amount of gasoline used to drive to the grocery store is a scalar quantity because it only needs one number: 2 gallons.
In this unit, we deal with vectors. A vector is a directed line segment -- that is, a line segment that points one direction or the other. As such, it has an initial point and a terminal point. The vector starts at the initial point and ends at the terminal point, and the vector points towards the terminal point. A vector is drawn as a line segment with an arrow at the terminal point:
The same vector can be placed anywhere on the coordinate plane and still be the same vector -- the only two bits of information a vector represents are the magnitude and the direction. The magnitude is simply the length of the vector, and the direction is the angle at which it points. Since neither of these specify a starting or ending location, the same vector can be placed anywhere. To illustrate, all of the line segments below can be defined as the vector with magnitude and angle 45 degrees:
It is customary, however, to place the vector with the initial point at the origin as indicated by the black vector. This is called the standard position.
In standard practice, we don't express vectors by listing the length and the direction. We instead use component form, which lists the height (rise) and width (run) of the vectors. It is written as follows:
Other ways of denoting a vector in component form include:
From the diagram we can now see the benefits of the standard position: the two numbers for the terminal point's coordinates are the same numbers for the vector's rise and run. Note that we named this vector . Just as you can assign numbers to variables in algebra (usually ), you can assign vectors to variables in calculus. The letters are usually used, and either boldface or an arrow over the letter is used to identify it as a vector.
When expressing a vector in component form, it is no longer obvious what the magnitude and direction are. Therefore, we have to perform some calculations to find the magnitude and direction.
where is the width, or run, of the vector; is the height, or rise, of the vector. You should recognize this formula as the Pythagorean theorem. It is -- the magnitude is the distance between the initial point and the terminal point.
The magnitude of a vector can also be called the norm.
Vector Addition is often called tip-to-tail addition, because this makes it easier to remember.
The sum of the vectors you are adding is called the resultant vector, and is the vector drawn from the initial point (tip) of the first vector to the terminal point (tail) of the second vector. Although they look like the arrows, the pointy bit is the tail, not the tip. (Imagine you were walking the direction the vector was pointing... you would start at the flat end (tip) and walk toward the pointy end.)
It looks like this:
(Notice, the black lined vector is the sum of the two dotted line vectors!)
Graphically, multiplying a vector by a scalar changes only the magnitude of the vector by that same scalar. That is, multiplying a vector by 2 will "stretch" the vector to twice its original magnitude, keeping the direction the same.
Numerically, you calculate the resultant vector with this formula:
, where is a constant scalar.
As previously stated, the magnitude is changed by the same constant:
Since multiplying a vector by a constant results in a vector in the same direction, we can reason that two vectors are parallel if one is a constant multiple of the other -- that is, that if for some constant .
We can also divide by a non-zero scalar by instead multiplying by the reciprocal, as with dividing regular numbers:
A depiction of the property of linearity with respect to a function applied to 2D vectors. In this figure, it is apparent that .
Given a function that accepts a vector as input and returns a vector or scalar as the output, function is considered to be "linear" if the following holds:
For any vectors and , it is the case that .
For any vector and scalar , it is the case that .
More generally, when given a function that has multiple vector valued parameters , function is a "multi-linear" function if is linear with respect to each parameter while holding all other parameters constant:
For each and vectors :
For any vector from the same vector space as , it is the case that
For any scalar , it is the case that
If , then is "bilinear". Bilinear functions include the dot product and the cross product.
The dot product is a way of multiplying two vectors to produce a scalar value. Because it combines the components of two vectors to form a /scalar/, it is sometimes called a scalar product. If you were asked to take the 'dot product of two rectangular vectors' you would do the following:
It is very important to note that the dot product of two vectors does not result in another vector, it gives you a scalar, just a numerical value.
Another common pitfall may arise if your vectors are not in rectangular ('cartesian') format. Sometimes, vectors are instead expressed in polar coordinates, where the first component is the vector's magnitude (length) and the second is the angle from the -axis at which the vector should be oriented. Dot products cannot be performed using the conventional method on these sorts of vectors; vectors in polar format must be converted to their equivalent rectangular form before you can work with them using the formula given above. A common way to convert to rectangular coordinates is to imagine that the vector was projected horizontally and vertically to form a right triangle. You could then use properties of sin and cos to find the length of the two legs the right triangle. The horizontal length would then be the x-component of the rectangular expression of the vector and the vertical length would be the y-component. Remember that if the vector is pointing down or to the left, the corresponding components would have to be negative to indicate that.
With some rearrangement and trigonometric manipulation, we can see that the number that results from the dot product of two vectors is a surprising and useful identity:
where is the angle between the two vectors. This provides a convenient way of finding the angle between two vectors:
Notice that the dot product is 'commutative', that is:
Also, the dot product of two vectors will be the length of the vector squared:
and by the Pythagorean theorem,
The dot product can be visualized as the length of a projection of one vector on to the other. In other words, the dot product asks 'how much magnitude of this vector is going in the direction of that vector?'
Start with the following definition for the dot product: where is the angle between and .
The formula can be derived from the above definition through various approaches:
The triangle .
One of the more direct approaches is to use the law of cosines. Create a triangle with vertices , , and . The displacement , the displacement , and the displacement . The angle .
The lengths of the sides of the triangle are , and , and . Applying the law of cosines gives:
A more intuitive derivation of uses the fact that the dot product is a bilinear operator. To establish that the dot product is a bilinear operator, the following must be established:
Holding constant, the dot product must be linear with respect to .
Holding constant, the dot product must be linear with respect to .
Since it is readily apparent from the definition that , linearity with respect to implies linearity with respect to . It is hence only necessary to establish that the dot product is linear with respect to to establish bilinearity.
The orthogonal projection of onto .
where is the "orthogonal projection" of vector onto a line whose direction is the direction of . is the component of that is parallel to . It is not hard to observe that is linear with respect to while is held constant. The dot product is linear with respect to , and therefore the dot product is a bilinear operator.
The bilinearity of the dot product now enables the derivation:
Applications of Scalar Multiplication and Dot ProductEdit
A unit vector is a vector with a magnitude of 1. The unit vector of u is a vector in the same direction as , but with a magnitude of 1:
The process of finding the unit vector of is called normalization. As mentioned in scalar multiplication, multiplying a vector by constant will result in the magnitude being multiplied by . We know how to calculate the magnitude of . We know that dividing a vector by a constant will divide the magnitude by that constant. Therefore, if that constant is the magnitude, dividing the vector by the magnitude will result in a unit vector in the same direction as :
A special case of Unit Vectors are the Standard Unit Vectors : points one unit directly right in the direction, and points one unit directly up in the direction:
Using the scalar multiplication and vector addition rules, we can then express vectors in a different way:
If we work that equation out, it makes sense. Multiplying by will result in the vector . Multiplying by will result in the vector . Adding these two together will give us our original vector, . Expressing vectors using is called standard form.
If the angle between two vectors is 90 degrees or (if the two vectors are orthogonal to each other), that is the vectors are perpendicular, then the dot product is 0. This provides us with an easy way to find a perpendicular vector: if you have a vector , a perpendicular vector can easily be found by either
Polar coordinates are an alternative two-dimensional coordinate system, which is often useful when rotations are important. Instead of specifying the position along the and axes, we specify the distance from the origin, , and the direction, an angle .
Looking at this diagram, we can see that the values of are related to those of and by the equations
Because tan-1 is multivalued, care must be taken to select the right value.
Just as for Cartesian coordinates the unit vectors that point in the and directions are special, so in polar coordinates the unit vectors that point in the and directions are also special.
We will call these vectors and , pronounced r-hat and theta-hat. Putting a circumflex over a vector this way is often used to mean the unit vector in that direction.
Two-dimensional Cartesian coordinates as we've discussed so far can be easily extended to three-dimensions by adding one more value: . If the standard coordinate axes are drawn on a sheet of paper, the -axis would extend upwards off of the paper.
Similar to the two coordinate axes in two-dimensional coordinates, there are three coordinate planes in space. These are the -plane, the -plane, and the -plane. Each plane is the "sheet of paper" that contains both axes the name mentions. For instance, the -plane contains both the and axes and is perpendicular to the -axis.
Therefore, vectors can be extended to three dimensions by simply adding the value.
To facilitate standard form notation, we add another standard unit vector:
Again, both forms (component and standard) are equivalent.
Magnitude: Magnitude in three dimensions is the same as in two dimensions, with the addition of a term in the radicand.
The polar coordinate system is extended into three dimensions with two different coordinate systems, the cylindrical and spherical coordinate systems, both of which include two-dimensional or planar polar coordinates as a subset. In essence, the cylindrical coordinate system extends polar coordinates by adding an additional distance coordinate, while the spherical system instead adds an additional angular coordinate.
The cylindrical coordinate system is a coordinate system that essentially extends the two-dimensional polar coordinate system by adding a third coordinate measuring the height of a point above the plane, similar to the way in which the Cartesian coordinate system is extended into three dimensions. The third coordinate is usually denoted , making the three cylindrical coordinates .
The three cylindrical coordinates can be converted to Cartesian coordinates by
Polar coordinates can also be extended into three dimensions using the coordinates , where is the distance from the origin, is the angle from the -axis (called the colatitude or zenith and measured from 0 to 180°) and is the angle from the -axis (as in the polar coordinates). This coordinate system, called the spherical coordinate system, is similar to the latitude and longitude system used for Earth, with the origin in the centre of Earth, the latitude being the complement of , determined by , and the longitude being measured by .
The three spherical coordinates are converted to Cartesian coordinates by
Start with the following definition for the cross product: . Vector is a unit length vector that is perpendicular to both and and oriented according to the "right hand rule". Angle is the counterclockwise angle from to within the plane that is orthogonal to .
The formula can be derived by exploiting the bilinearity of the cross product. To establish the cross product as a bilinear operator, the following must be established:
Holding constant, the cross product must be linear with respect to .
Holding constant, the cross product must be linear with respect to .
From the definition , the anticommutative property of the cross product can be inferred: (vector reverses direction when and are swapped). This means that linearity with respect to implies linearity with respect to . It is hence only necessary to establish that the cross product is linear with respect to to establish bilinearity.
The perpendicular component of relative to the line .
is the component of that is perpendicular to a line whose direction is the direction of . where vector is a unit length vector that is parallel to .
is a 90 degree counterclockwise rotation of around . Note that .
It can easily be observed that is linear with respect to with held constant, and that is linear with respect to with held constant. The cross product is linear with respect to , and therefore the cross product is a bilinear operator.
The bilinearity of the cross product now enables the derivation:
Vector-Valued Functions are functions that instead of giving a resultant scalar value, give a resultant vector value. These aid in the creation of direction and vector fields, and are therefore used in physics to aid with visualizations of electric, magnetic, and many other types of fields. They are of the following form:
Put simply, the limit of a vector-valued function is the limit of its parts.
Therefore for any there is a such that
But by the triangle inequality
A similar argument can be used through parts .
Now let again, and that for any there is a corresponding such implies
From this we can then create an accurate definition of a derivative of a vector-valued function:
The final step was accomplished by taking what we just did with limits.
By the Fundamental Theorem of Calculus integrals can be applied to the vector's components.
In other words: the limit of a vector function is the limit of its parts, the derivative of a vector function is the derivative of its parts, and the integration of a vector function is the integration of it parts.
Velocity, Acceleration, Curvature, and a brief mention of the BinormalEdit
Assume we have a vector-valued function which starts at the origin and as its independent variables changes the points that the vectors point at trace a path.
We will call this vector , which is commonly known as the position vector.
If then represents a position and t represents time, then in model with Physics we know the following:
where is the velocity vector.
is the speed.
where is the acceleration vector.
The only other vector that comes in use at times is known as the curvature vector.
The vector used to find it is known as the unit tangent vector, which is defined as or shorthand .
The vector normal to this then is .
We can verify this by taking the dot product
Also note that
Then we can actually verify:
Therefore is perpendicular to
What this gives rise to is the Unit Normal Vector of which the top-most vector is the Normal vector, but the bottom half is known as the curvature. Since the Normal vector points toward the inside of a curve, the sharper a turn, the Normal vector has a large magnitude, therefore the curvature has a small value, and is used as an index in civil engineering to reflect the sharpness of a curve (clover-leaf highways, for instance).
The only other thing not mentioned is the Binormal that occurs in 3-d curves , which is useful in creating planes parallel to the curve.