A vector is a mathematical concept that has both magnitude and direction. Detailed explanation of vectors may be found at Wikibooks linear algebra. In physics, vectors are used to describe things happening in space by giving a series of quantities which relate to the problem's coordinate system.
A vector is often expressed as a series of numbers. For example, in the two-dimensional space of real numbers, the notation (1, 1) represents a vector that is pointed 45 degrees from the x-axis towards the y-axis with a magnitude of .
Commonly in physics, we use position vectors to describe where something is in the space we are considering, or how its position is changing at that moment in time. Position vectors are written as summations of scalars multiplied by unit vectors. For example:
where x, y and z are scalars and and are unit vectors of the Cartesian(René Descartes) coordinate system. A unit vector is a special vector which has magnitude 1 and points along one of the coordinate frame's axes. Unit vectors for each direction can be written as either or . The figure to the left illustrates this with . A vector itself is typically indicated by either an arrow: , or just by boldface type: v, so the vector above as a complete equation would be denoted as:
This velocity vector follows the convention that subscripts denote the components of the velocity vector. Writing the components of as would be more consistent but is almost never done.
You can find the magnitude of a vector with this formula . For example, in two-dimensional space, this equation reduces to:
For three-dimensional space, this equation becomes:
Many problems, particularly in mechanics, involve the use of two- or three-dimensional space to describe where objects are and what they are doing. Vectors can be used to condense this information into a precise and easily understandable form that is easy to manipulate with mathematics.
Position - or where something is, can be shown using a position vector. Position vectors measure how far something is from the origin of the reference frame and in what direction, and are usually, though not always, given the symbol . It is usually good practice to use for position vectors when describing your solution to a problem as most physicists use this notation.
Velocity is defined as the rate of change of position with respect to time. You may be used to writing velocity, v, as a scalar because it was assumed in your solution that v referred to speed in the direction of travel. However, if we take the strict definition and apply it to the position vector, which is usually written:
Taking the time derivative:
We did not take the derivatives of the unit vectors because they are not changing. If the unit vectors are rotating, it is possible to take (vector) derivatives of them and derive the Coriolis force
Another example of an alternative coordinate system is the rotated coordinate system:
Click here for a derivation of the rotation matrix
Rotation of coordinates
From Wikipedia, only a single angle is needed to specify a rotation in two dimensions – the angle of rotation, as defined in the figure. This figure depicts a passive (or alias) transformation in which the point remains stationary while the basis vectors are rotated.
While such transformations will not often be used, a deep understanding of them will yield insights relevant to relativity and quantum mechanics. It is important to know how this transformation can be derived as a matrix transformation. For that reason we will carefully derive how the primed and unprimed coordinates are related when:
We begin by considering the active (or alibi) transformation of the unit vectors. Using well known and elementary properties concerning the components of a vector, we have:
These two equations are valuable because they allow us to calculate dot products between the primed and unprimed basis vectors:
These dot products allow us to solve for any component of the primed or unprimed components of a vector. For example to find we take the dot product with :
simplifying, we have
This result can also be obtained without using unit vectors. Note how we used the dot product (also called inner product) to simplify the equations, using the property that the inner product between orthogonal vectors vanish. This concept can also be applied to infinite dimensional vector spaces, including functions, which under certain circumstances can be viewed as vectors. The results of such generalizations include Fourier transforms and the bra/ket notation of quantum mechanics.
Other permutations of this procedure yield two matrix equations:
The vector product (or cross product) of two vectors and is another vector defined as
where is the angle between and , and is a unit vector perpendicular to the plane containing and in the right-handed sense (see Figure 3 for a geometric interpretation)
Figure 3: Vector product of two vectors.
In terms of the orthonormal basis , the cross product can be written in the form of a determinant
In index notation, the cross product can be written as
where is the Levi-Civita symbol (also called the permutation symbol, alternating tensor). This latter expression is easy to remember if you recognize that xyz, yzx, and zxy are "positive" and the others are negative: xzy, yxz, zyx.
Advanced topic:Basis vectors that are not orthonormal
This material is a bit advanced and need not be read by beginners.
Figure 1: A vector and its basis. bj=a·gj/gj is the projection of a on gj but is not a component if gj are the basis vectors (j=1,2)
You could also represent the same vector in terms of another set of basis vectors () as shown in Figure 1(b):
This space has some complications that we shall not go into. These complications involve the fact that if the basis vectors are not orthonormal, it is best to define a different "dual" basis to accompany (). This "dual" space uses superscripts instead of subscripts on the vectors: (). To understand this difficulty, suppose in the figure that the subscripted basis vectors are of unit magnitude: .
Instead, we define a dual basis (with superscripts on the vectors) so that:
In solid state physics it is convenient for the basis vectors to represent displacements among nearest neighboring atoms. The basis vectors are neither normal nor orthogonal.
The five fundamental two-dimensional Bravais lattices: 1 oblique, 2 rectangular, 3 centered rectangular (rhombic), 4 hexagonal, and 5 square