Basic Definitions
A scalar represents magnitude, and is given by a real number: .
A point in ndimensional space is given by an ntuple where each coordinate p_{i} is a scalar number. We will write as a shorthand for this ntuple. The position of a point is relative to a coordinate system with an origin and unit axes , , ..., and . Thus, a 3dimensional (3D) point can be given by a triple whose coordinates are relative to the axes (1,0,0), (0,1,0), and (0,0,1) which are commonly referred to as the x, y, and zaxes. Because of this established convention, we sometimes write for 2D points and for 3D points.
A vector represents magnitude and direction in space, and is given by an ntuple where each coordinate v_{i} is a scalar. We also write as a shorthand for the vector's ntuple. The vector v is interpreted to be the magnitude and direction of the line segment going from the origin to the point . However, the vector is not this point, which only gives a standard way of visualizing the vector. We can also visualize the vector as a directed line segment from any initial point to a final point. Then, the vector from P to Q is given by:



Point Difference Definition





showing that the difference of any two points is considered to be a vector. So, vectors do not have a fixed position in space, but can be located at any initial base point P. For example, a traveling vehicle can be said to be going east (direction) at 50 mph (magnitude) no matter where it is located. We could even visualize a field of vectors, one at each point of a space; such as vectors for the wind direction and speed at each point on the surface of the earth.
Vector Addition
The sum of two vectors is given by adding their corresponding coordinates. So, for vectors and , we have:



Vector Addition Definition





One can also add a vector to a point to get another point, given by:



Point+Vector Addition Definition





The resulting point Q is considered to be the displacement, or “translation”, of the point P in the direction of and by the magnitude of the vector .
Vector addition satisfies the following properties:
 [Vector Addition Association]
 [Commutation]
 [Point+Vector Association]
The property of “Point+Vector Association” means that result of translating a point by two sequentially applied vector displacements is the same as a single translation by the sum of those two vectors.
Scalar Multiplication
Multiplication of a vector by a real number a (called a scalar) is given by:



Scalar Multiply Definition





This represents scaling the size of a vector by a magnification factor of a. So, for example, 2v is twice the size of v, and v/2 = (1/2)v is half of v.
Scalar multiplication has the properties:
 [Scalar Association]
 [Scalar Distribution]
 [Vector Distribution]
Scalar multiplication can be used to interpolate positions between two points P and Q. To get an intermediate point R between P and Q, given by a fractional ratio r, one first scales the vector to rv, and adds it to P to get: . For example, to get the midpoint M between P and Q, use to compute .
This interpolation equation is also used to represent the line through P and Q as a function of a (1dimensonal) scalar parameter, namely as: which is the parametric line equation. It is valid because one can use “affine addition” to combine points in a coordinatefree manner.
Affine Addition
We have already seen that the difference between two points can be considered as a vector. However, in general, it makes no sense to add two points together. Points denote an absolute position in space independent of any coordinate system describing them. Blindly adding individual coordinates together would give different answers for different coordinate reference frames.
Nevertheless, there is one special case, known as affine addition, where one can add points together as a weighted sum. In fact, in the previous section on scalar multiplication, we did just that to represent the points on a line going through two fixed points P and Q. More generally, given m points P_{0}_{ }, …, P_{m–1}, one can define:



Affine Sum Definition





where the coefficients, which can be any real number (positive or negative), must add up to 1. One interprets this sum as the center of mass for weights a_{i} located at the points P_{i}. A negative coefficient can be thought of as a negative mass, like a helium balloon. This center of mass is uniquely determined in absolute space regardless of what coordinate system and frame of reference is being used. The affine sum has the property:
 [Translation Invariance]
Since the line equation is an affine sum, given equal weights , we have that is the midpoint of the line segment from P_{0} to P_{1}. Further, every point on the line through P_{0}_{ }and P_{1} is uniquely represented by a pair with , which results in the parametric line equation: where each point is represented by a unique real number t.
Similarly, in 3D space, the affine sum of three noncollinear points P_{0}, P_{1}, P_{2} defines a point in the plane going through these points. So, every point P in the plane of the triangle is uniquely represented by a triple with . This triple is called the barycentric coordinate of its associated point P on the plane with respect to . Further, by substituting a_{1} = s, a_{2} = t, and , one can write the parametric plane equation as: , where and are independent vectors spanning the plane. The pair (s, t) is called the parametric coordinate of P relative to , and there is a unique parametric coordinate for each point of the plane.
Vector Length
The length of a vector v is denoted by , and is defined as:



Vector Length Definition





This gives the standard Euclidean geometry (Pythagorean) length for the line segment representing a vector. For a 2D vector , one has: , which is the Pythagorean theorem for the diagonal of a rectangle.
Vector length has the following properties:
 [Scalar magnification]
 [Triangle Inequality]
 [The Cosine Law, Euclid II, Prop 12&13]
A unit vector is one whose length = 1. One can scale any vector v to get a unit vector u that points in the same direction as v by computing: , for which u = 1. The process of scaling v to a unit vector u is called normalization, and one says that v has been normalized. One thinks of u as the direction of v since simply scales u to the magnitude v.
