Vectors

=Euclidean vector = This article is about the vectors mainly used in physics and engineering to represent directed quantities. For mathematical vectors in general, see vector space. For other uses, see vector.

Illustration of a vector

A vector going from //A// to //B// In elementary mathematics, physics, and engineering, a **vector** (sometimes called a **geometric**[1] or **spatial vector**[2]) is a geometric object that has both a magnitude (or length) and direction. A vector is frequently represented by a line segment with a definite direction, or graphically as an arrow, connecting an **initial point** //A// with a **terminal point** //B//,[3] and denoted by A vector is what is needed to "carry" the point //A// to the point //B//; the Latin word //vector// means "one who carries".[4] The magnitude of the vector is the distance between the two points and the direction refers to the direction of displacement from //A// to //B//. Many algebraic operations on real numbers such as addition, subtraction, multiplication, and negation have close analogues for vectors, operations which obey the familiar algebraic laws of commutativity, associativity, and distributivity. These operations and associated laws qualify Euclidean vectors as an example of the more generalized concept of a vector space. Vectors play an important role in physics: velocity and acceleration of a moving object and forces acting on a body are all described by vectors. Many other physical quantities can be usefully thought of as vectors. The mathematical representation of a physical vector depends on the coordinate system used to describe it. Other vector-like objects that describe physical quantities and transform in a similar way under changes of the coordinate system include pseudovectors and tensors.

Overview
A vector is a geometric entity characterized by a magnitude (in mathematics a number, in physics a number times a unit) and a direction. In rigorous mathematical treatments,[5] a vector is defined as a directed line segment, or arrow, in a Euclidean space. When it becomes necessary to distinguish it from vectors as defined elsewhere, this is sometimes referred to as a **geometric**, **spatial**, or **Euclidean** vector. As an arrow in Euclidean space, a vector possesses a definite **initial point** and **terminal point**. Such a vector is called a **bound vector**. In other situations, when only the magnitude and direction of the vector matter, then the particular initial point is of no importance, and the vector is called a **free vector**. Thus two arrows and in space represent the same free vector if they have the same magnitude and direction: equivalently, they are equivalent if the quadrilateral //ABB′A′// is a parallelogram. If the Euclidean space is equipped with a choice of origin, then a free vector is equivalent to the bound vector of the same magnitude and direction whose initial point is the origin. The term //vector// also has generalizations to higher dimensions and to more formal approaches with much wider applications. Such generalizations are found in other articles

Examples in one dimension
A force may be "15 N to the right", with coordinate 15 N if the basis vector is to the right, and −15 N if the basis vector is to the left. The magnitude of the vector is 15 N in both cases. A displacement may be "4 m to the right", with coordinate 4 m if the basis vector is to the right, and −4 m if the basis vector is to the left. The magnitude of the vector is 4 m in both cases. The work done by the force in the case of this displacement is 60 J in both cases.

Use in physics and engineering
Vectors are fundamental in the physical sciences. They can be used to represent any quantity that has both a magnitude and direction, such as velocity, the magnitude of which is speed. For example, the velocity //5 meters per second upward// could be represented by the vector (0,5) (in 2 dimensions with the positive y axis as 'up'). Another quantity represented by a vector is force, since it has a magnitude and direction. Vectors also describe many other physical quantities, such as displacement, acceleration, momentum, and angular momentum. Other physical vectors, such as the electric and magnetic field, are represented as a system of vectors at each point of a physical space; that is, a vector field.

Vectors in Cartesian space
In the Cartesian coordinate system, a vector can be represented by identifying the coordinates of its initial and terminal point. For instance, the points //A// = (1,0,0) and //B// = (0,1,0) in space determine the free vector pointing from the point //x//=1 on the //x//-axis to the point //y//=1 on the //y//-axis. Typically in Cartesian coordinates, one considers primarily bound vectors. A bound vector is determined by the coordinates of the terminal point, its initial point always having the coordinates of the origin //O// = (0,0,0). Thus the bound vector represented by (1,0,0) is a vector of unit length pointing from the origin up the positive //x//-axis. The coordinate representation of vectors allows the algebraic features of vectors to be expressed in a convenient numerical fashion. For example, the sum of the vectors (1,2,3) and (−2,0,4) is the vector

Euclidean vectors and affine vectors
In the geometrical and physical settings, sometimes it is possible to associate, in a natural way, a //length// or magnitude and a direction to vectors. In turn, the notion of direction is strictly associated with the notion of an //angle// between two vectors. When the length of vectors is defined, it is possible to also define a dot product — a scalar-valued product of two vectors — which gives a convenient algebraic characterization of both length (the square root of the dot product of a vector by itself) and angle (a function of the dot product between any two vectors). In three dimensions, it is further possible to define a cross product which supplies an algebraic characterization of the area and orientation in space of the parallelogram defined by two vectors (used as sides of the parallelogram). However, it is not always possible or desirable to define the length of a vector in a natural way. This more general type of spatial vector is the subject of vector spaces (for bound vectors) and affine spaces (for free vectors).

Generalizations
In physics, as well as mathematics, a vector is often identified with a tuple, or list of numbers, which depend on some auxiliary coordinate system or reference frame. When the coordinates are transformed, for example by rotation or stretching, then the components of the vector also transform. The vector itself has not changed, but the reference frame has, so the components of the vector (or measurements taken with respect to the reference frame) must change to compensate. The vector is called //covariant// or //contravariant// depending on how the transformation of the vector's components is related to the transformation of coordinates. See covariance and contravariance of vectors. Tensors are another type of quantity that behave in this way; in fact a vector is a special type of tensor. In pure mathematics, a vector is any element of a vector space over some field and is often represented as a coordinate vector. The vectors described in this article are a very special case of this general definition because they are contravariant with respect to the ambient space. Contravariance captures the physical intuition behind the idea that a vector has "magnitude and direction".

Representation of a vector
Vectors are usually denoted in lowercase boldface, as **a** or lowercase italic boldface, as **//a//**. (Uppercase letters are typically used to represent matrices.) Other conventions include or //__a__//, especially in handwriting. Alternately, some use a tilde (~) or a wavy underline drawn beneath the symbol, which is a convention for indicating boldface type. If the vector represents a directed distance or displacement from a point //A// to a point //B// (see figure), it can also be denoted as or //__AB__//. Vectors are usually shown in graphs or other diagrams as arrows (directed line segments), as illustrated in the figure. Here the point //A// is called the //origin//, //tail//, //base//, or //initial point//; point //B// is called the //head//, //tip//, //endpoint//, //terminal point// or //final point//. The length of the arrow is proportional to the vector's magnitude, while the direction in which the arrow points indicates the vector's direction. On a two-dimensional diagram, sometimes a vector perpendicular to the plane of the diagram is desired. These vectors are commonly shown as small circles. A circle with a dot at its centre (Unicode U+2299 ⊙ ) indicates a vector pointing out of the front of the diagram, toward the viewer. A circle with a cross inscribed in it (Unicode U+2297 ⊗ ) indicates a vector pointing into and behind the diagram. These can be thought of as viewing the tip of an arrow head on and viewing the vanes of an arrow from the back.

A vector in the Cartesian plane, showing the position of a point //A// with coordinates (2,3). In order to calculate with vectors, the graphical representation may be too cumbersome. Vectors in an //n//-dimensional Euclidean space can be represented in a Cartesian coordinate system. The endpoint of a vector can be identified with an ordered list of //n// real numbers (//n//-tuple). As an example in two dimensions (see figure), the vector from the origin //O// = (0,0) to the point //A// = (2,3) is simply written as The notion that the tail of the vector coincides with the origin is implicit and easily understood. Thus, the more explicit notation is usually not deemed necessary and very rarely used. In three dimensional Euclidean space (or ), vectors are identified with triples of numbers corresponding to the Cartesian coordinates of the endpoint (//a//,//b//,//c//): These numbers are often arranged into a column vector or row vector, particularly when dealing with matrices, as follows: [[image:file:///C:/DOCUME%7E1/PERSONAL/LOCALS%7E1/Temp/msohtmlclip1/01/clip_image022.gif width="67" height="73" caption="\mathbf{a} = \begin{bmatrix} a\\ b\\ c\\ \end{bmatrix}"]] Another way to express a vector in three dimensions is to introduce the three standard basis vectors: These have the intuitive interpretation as vectors of unit length pointing up the //x//, //y//, and //z// axis of a Cartesian coordinate system, respectively, and they are sometimes referred to as versors of those axes. In terms of these, any vector in can be expressed in the form: In introductory physics classes, these three special vectors are often instead denoted, the versors of the three dimensional space (or ), in which the hat symbol (^) typically denotes unit vectors (vectors with unit length). The notation **e**//i// is compatible with the index notation and the summation convention commonly used in higher level mathematics, physics, and engineering. The use of Cartesian versors such as as a basis in which to represent a vector is not mandated. Vectors can also be expressed in terms of cylindrical unit vectors or spherical unit vectors. The latter two choices are more convenient for solving problems which possess cylindrical or spherical symmetry respectively.

Basic properties
The following section uses the Cartesian coordinate system with basis vectors and assume that all vectors have the origin as a common base point. A vector **a** will be written as

Vector equality
Two vectors are said to be equal if they have the same magnitude and direction. Equivalently they will be equal if their coordinates are equal. So two vectors and are equal if

Addition and subtraction
Assume now that **a** and **b** are not necessarily equal vectors, but that they may have different magnitudes and directions. The sum of **a** and **b** is [[image:file:///C:/DOCUME%7E1/PERSONAL/LOCALS%7E1/Temp/msohtmlclip1/01/clip_image035.gif width="404" height="21" caption="\mathbf{a}+\mathbf{b} =(a_1+b_1)\mathbf{e_1} +(a_2+b_2)\mathbf{e_2} +(a_3+b_3)\mathbf{e_3}."]] The addition may be represented graphically by placing the start of the arrow **b** at the tip of the arrow **a**, and then drawing an arrow from the start of **a** to the tip of **b**. The new arrow drawn represents the vector **a** + **b**, as illustrated below: This addition method is sometimes called the //parallelogram rule// because **a** and **b** form the sides of a parallelogram and **a** + **b** is one of the diagonals. If **a** and **b** are bound vectors that have the same base point, it will also be the base point of **a** + **b**. One can check geometrically that **a** + **b** = **b** + **a** and (**a** + **b**) + **c** = **a** + (**b** + **c**). The difference of **a** and **b** is [[image:file:///C:/DOCUME%7E1/PERSONAL/LOCALS%7E1/Temp/msohtmlclip1/01/clip_image038.gif width="405" height="21" caption="\mathbf{a}-\mathbf{b} =(a_1-b_1)\mathbf{e_1} +(a_2-b_2)\mathbf{e_2} +(a_3-b_3)\mathbf{e_3}."]] Subtraction of two vectors can be geometrically defined as follows: to subtract **b** from **a**, place the ends of **a** and **b** at the same point, and then draw an arrow from the tip of **b** to the tip of **a**. That arrow represents the vector **a** − **b**, as illustrated below:

Scalar multiplication


Scalar multiplication of a vector by a factor of 3 stretches the vector out.

The scalar multiplications 2**a** and −**a** of a vector **a** A vector may also be multiplied, or re-//scaled//, by a real number //r//. In the context of conventional vector algebra, these real numbers are often called **scalars** (from //scale//) to distinguish them from vectors. The operation of multiplying a vector by a scalar is called **scalar multiplication**. The resulting vector is [[image:file:///C:/DOCUME%7E1/PERSONAL/LOCALS%7E1/Temp/msohtmlclip1/01/clip_image045.gif width="284" height="21" caption="r\mathbf{a}=(ra_1)\mathbf{e_1} +(ra_2)\mathbf{e_2} +(ra_3)\mathbf{e_3}."]] Intuitively, multiplying by a scalar //r// stretches a vector out by a factor of //r//. Geometrically, this can be visualized (at least in the case when //r// is an integer) as placing //r// copies of the vector in a line where the endpoint of one vector is the initial point of the next vector. If //r// is negative, then the vector changes direction: it flips around by an angle of 180°. Two examples (//r// = −1 and //r// = 2) are given below: Scalar multiplication is distributive over vector addition in the following sense: //r//(**a** + **b**) = //r//**a** + //r//**b** for all vectors **a** and **b** and all scalars //r//. One can also show that **a** − **b** = **a** + (−1)**b**.

Length of a vector
The **length** or **magnitude** or **norm** of the vector **a** is denoted by ||**a**|| or, less commonly, |**a**|, which is not to be confused with the absolute value (a scalar "norm"). The length of the vector **a** can be computed with the Euclidean norm which is a consequence of the Pythagorean theorem since the basis vectors **e1**, **e2**, **e3** are orthogonal unit vectors. This happens to be equal to the square root of the dot product of the vector with itself:

Dot product
The **dot product** of two vectors **a** and **b** (sometimes called the **inner product**, or, since its result is a scalar, the **scalar product**) is denoted by **a** ∙ **b** and is defined as: [[image:file:///C:/DOCUME%7E1/PERSONAL/LOCALS%7E1/Temp/msohtmlclip1/01/clip_image048.gif width="172" height="20" caption="\mathbf{a}\cdot\mathbf{b} =\left\|\mathbf{a}\right\|\left\|\mathbf{b}\right\|\cos\theta"]] where //θ// is the measure of the angle between **a** and **b** (see trigonometric function for an explanation of cosine). Geometrically, this means that **a** and **b** are drawn with a common start point and then the length of **a** is multiplied with the length of that component of **b** that points in the same direction as **a**. The dot product can also be defined as the sum of the products of the components of each vector as

Unit vector
A **unit vector** is any vector with a length of one; normally unit vectors are used simply to indicate direction. A vector of arbitrary length can be divided by its length to create a unit vector. This is known as **normalizing** a vector. A unit vector is often indicated with a hat as in **â**. The normalization of a vector **a** into a unit vector **â** To normalize a vector **a** = [//a//1, //a//2, //a//3], scale the vector by the reciprocal of its length ||**a**||. That is:

Null vector
Main article: Null vector The **null vector** (or **zero vector**) is the vector with length zero. Written out in coordinates, the vector is (0,0,0), and it is commonly denoted, or **0**, or simply 0. Unlike any other vector, it does not have a direction, and cannot be normalized (that is, there is no unit vector which is a multiple of the null vector). The sum of the null vector with any vector **a** is **a** (that is, **0**+**a**=**a**).

Cross product
Main article: Cross product The **cross product** (also called the **vector product** or **outer product**) is only meaningful in three or seven dimensions. The cross product differs from the dot product primarily in that the result of the cross product of two vectors is a vector. The cross product, denoted **a** × **b**, is a vector perpendicular to both **a** and **b** and is defined as [[image:file:///C:/DOCUME%7E1/PERSONAL/LOCALS%7E1/Temp/msohtmlclip1/01/clip_image054.gif width="208" height="20" caption="\mathbf{a}\times\mathbf{b} =\left\|\mathbf{a}\right\|\left\|\mathbf{b}\right\|\sin(\theta)\,\mathbf{n}"]] where //θ// is the measure of the angle between **a** and **b**, and **n** is a unit vector perpendicular to both **a** and **b** which completes a right-handed system. The right-handedness constraint is necessary because there exist //two// unit vectors that are perpendicular to both **a** and **b**, namely, **n** and (–**n**).

An illustration of the cross product. The cross product **a** × **b** is defined so that **a**, **b**, and **a** × **b** also becomes a right-handed system (but note that **a** and **b** are not necessarily orthogonal). This is the right-hand rule. The length of **a** × **b** can be interpreted as the area of the parallelogram having **a** and **b** as sides. The cross product can be written as For arbitrary choices of spatial orientation (that is, allowing for left-handed as well as right-handed coordinate systems) the cross product of two vectors is a pseudovector instead of a vector (see below).

Scalar triple product
Main article: Scalar triple product The **scalar triple product** (also called the **box product** or **mixed triple product**) is not really a new operator, but a way of applying the other two multiplication operators to three vectors. The scalar triple product is sometimes denoted by (**a** **b** **c**) and defined as: [[image:file:///C:/DOCUME%7E1/PERSONAL/LOCALS%7E1/Temp/msohtmlclip1/01/clip_image058.gif width="179" height="21" caption="(\mathbf{a}\ \mathbf{b}\ \mathbf{c}) =\mathbf{a}\cdot(\mathbf{b}\times\mathbf{c})."]] It has three primary uses. First, the absolute value of the box product is the volume of the parallelepiped which has edges that are defined by the three vectors. Second, the scalar triple product is zero if and only if the three vectors are linearly dependent, which can be easily proved by considering that in order for the three vectors to not make a volume, they must all lie in the same plane. Third, the box product is positive if and only if the three vectors **a**, **b** and **c** are right-handed. In components (//with respect to a right-handed orthonormal basis//), if the three vectors are thought of as rows (or columns, but in the same order), the scalar triple product is simply the determinant of the 3-by-3 matrix having the three vectors as rows The scalar triple product is linear in all three entries and anti-symmetric in the following sense: [[image:file:///C:/DOCUME%7E1/PERSONAL/LOCALS%7E1/Temp/msohtmlclip1/01/clip_image060.gif width="551" height="20" caption="(\mathbf{a}\ \mathbf{b}\ \mathbf{c}) = (\mathbf{c}\ \mathbf{a}\ \mathbf{b}) = (\mathbf{b}\ \mathbf{c}\ \mathbf{a})= -(\mathbf{a}\ \mathbf{c}\ \mathbf{b})  = -(\mathbf{b}\ \mathbf{a}\ \mathbf{c})  = -(\mathbf{c}\ \mathbf{b}\ \mathbf{a})."]]

Multiple Cartesian bases
All examples thus far have dealt with vectors expressed in terms of the same basis, namely, **e1**,**e2**,**e3**. However, a vector can be expressed in terms of any number of different bases that are not necessarily aligned with each other, and still remain the same vector. For example, using the vector **a** from above, [[image:file:///C:/DOCUME%7E1/PERSONAL/LOCALS%7E1/Temp/msohtmlclip1/01/clip_image061.gif width="364" height="16" caption="\mathbf{a} = a_1\mathbf{e}_1 + a_2\mathbf{e}_2 + a_3\mathbf{e}_3 = u\mathbf{n}_1 + v\mathbf{n}_2 + w\mathbf{n}_3"]] where **n1**,**n2**,**n3** form another orthonormal basis not aligned with **e1**,**e2**,**e3**. The values of //u//, //v//, and //w// are such that the resulting vector sum is exactly **a**. It is not uncommon to encounter vectors known in terms of different bases (for example, one basis fixed to the Earth and a second basis fixed to a moving vehicle). In order to perform many of the operations defined above, it is necessary to know the vectors in terms of the same basis. One simple way to express a vector known in one basis in terms of another uses column matrices that represent the vector in each basis along with a third matrix containing the information that relates the two bases. For example, in order to find the values of //u//, //v//, and //w// that define **a** in the **n1**,**n2**,**n3** basis, a matrix multiplication may be employed in the form where each matrix element //cjk// is the direction cosine relating **n**//j// to **e**//k//.[6] The term //direction cosine// refers to the cosine of the angle between two unit vectors, which is also equal to their dot product.[6] By referring collectively to **e1**,**e2**,**e3** as the //e// basis and to **n1**,**n2**,**n3** as the //n// basis, the matrix containing all the //cjk// is known as the "**transformation matrix** from //e// to //n//", or the "**rotation matrix** from //e// to //n//" (because it can be imagined as the "rotation" of a vector from one basis to another), or the "**direction cosine matrix** from //e// to //n//"[6] (because it contains direction cosines). The properties of a rotation matrix are such that its inverse is equal to its transpose. This means that the "rotation matrix from //e// to //n//" is the transpose of "rotation matrix from //n// to //e//". By applying several matrix multiplications in succession, any vector can be expressed in any basis so long as the set of direction cosines is known relating the successive bases.[6]

Other dimensions
With the exception of the cross and triple products, the above formula generalise to two dimensions and higher dimensions. For example, addition generalises to two dimensions the addition of and in four dimension [[image:file:///C:/DOCUME%7E1/PERSONAL/LOCALS%7E1/Temp/msohtmlclip1/01/clip_image064.gif width="700" height="49" caption="\begin{align}(a_1{\mathbf e}_1 + a_2{\mathbf e}_2 + a_3{\mathbf e}_3 + a_4{\mathbf e}_4)& + (b_1{\mathbf e}_1 + b_2{\mathbf e}_2 + b_3{\mathbf e}_3 + b_4{\mathbf e}_4)\\ &= (a_1+b_1){\mathbf e}_1 + (a_2+b_2){\mathbf e}_2 + (a_3+b_3){\mathbf e}_3 + (a_4+b_4){\mathbf e}_4.\end{align}"]] The cross product generalises to the exterior product, whose result is a bivector, which in general is not a vector. In two dimensions this is simply a scalar The seven dimensional cross product is similar to the cross product in that its result is a seven-dimensional vector orthogonal to the two arguments.

Vectors in physics
Vectors have many uses in physics and other sciences.

Vector length and units
In abstract vector spaces, the length of the arrow depends on a dimensionless scale. If it represents, for example, a force, the "scale" is of physical dimension length/force. Thus there is typically consistency in scale among quantities of the same dimension, but otherwise scale ratios may vary; for example, if "1 newton" and "5 m" are both represented with an arrow of 2 cm, the scales are 1:250 and 1 m:50 N respectively. Equal length of vectors of different dimension has no particular significance unless there is some proportionality constant inherent in the system that the diagram represents. Also length of a unit vector (of dimension length, not length/force, etc.) has no coordinate-system-invariant significance.

Vector-valued functions
Often in areas of physics and mathematics, a vector evolves in time, meaning that it depends on a time parameter //t//. For instance, if **r** represents the position vector of a particle, then **r**(//t//) gives a parametric representation of the trajectory of the particle. Vector-valued functions can differentiated and integrated by differentiating or integrating the components of the vector, and many of the familiar rules from calculus continue to hold for the derivative and integral of vector-valued functions.

Position, velocity and acceleration
The position of a point **x**=(//x//1, //x//2, //x//3) in three dimensional space can be represented as a position vector whose base point is the origin The position vectors has dimensions of length. Given two points **x**=(//x//1, //x//2, //x//3), **y**=(//y//1, //y//2, //y//3) their displacement is a vector which specifies the position of //y// relative to //x//. The length of this vector gives the straight line distance from //x// to //y//. Displacement has the dimensions of length. The velocity **v** of a point or particle is a vector, its length gives the speed. For constant velocity the position at time //t// will be where **x**0 is the position at time //t//=0. Velocity is the time derivative of position. Its dimensions are length/time. Acceleration **a** of a point is vector which is the time derivative of velocity. Its dimensions are length/time2.

Force, energy, work
Force is a vector with dimensions of mass×length/time2 and Newton's second law is the scalar multiplication Work is the dot product of force and displacement

Vector components


Illustration of tangential and normal components of a vector to a surface. A **component** of a vector is the influence of that vector in a given direction. [1] Components are themselves vectors. A vector is often described by a fixed number of components that sum up into this vector uniquely and totally. When used in this role, the choice of their constituting directions is dependent upon the particular coordinate system being used, such as Cartesian coordinates, spherical coordinates or polar coordinates. For example, an **axial component** of a vector is a component whose direction is determined by a projection onto one of the Cartesian coordinate axes, whereas **radial** and **tangential components** relate to the //radius of rotation// of an object as their direction of reference. The former is parallel to the radius and the latter is orthogonal to it. [2] Both remain orthogonal to the //axis of rotation// at all times. (In two dimensions this requirement becomes redundant as the axis degenerates to a //point of rotation.//) The choice of a coordinate system doesn't affect properties of a vector or its behaviour under transformations.

Vectors as directional derivatives
A vector may also be defined as a **directional derivative**: consider a function //f//(//x//α) and a curve //x//α(τ). Then the directional derivative of //f// is a scalar defined as where the index α is summed over the appropriate number of dimensions (for example, from 1 to 3 in 3-dimensional Euclidean space, from 0 to 3 in 4-dimensional spacetime, etc.). Then consider a vector tangent to //x//α(τ) : The directional derivative can be rewritten in differential form (without a given function //f// ) as Therefore any directional derivative can be identified with a corresponding vector, and any vector can be identified with a corresponding directional derivative. A vector can therefore be defined precisely as

Vectors, pseudo vectors, and transformations
An alternative characterization of Euclidean vectors, especially in physics, describes them as lists of quantities which behave a certain way under a coordinate transformation. A //contravariant vector// is required to have components that "transform like the coordinates" under changes of coordinates such as rotation and dilation. The vector itself does not change under these operations; instead, the components of the vector make a change that cancels the change in the spatial axes, in the same way that co-ordinates change. In other words, if the reference axes were rotated in one direction, the component representation of the vector would rotate in exactly the opposite way. Similarly, if the reference axes were stretched in one direction, the components of the vector, like the co-ordinates, would reduce in an exactly compensating way. Mathematically, if the coordinate system undergoes a transformation described by an invertible matrix //M//, so that a coordinate vector **x** is transformed to **x**′ = //M//**x**, then a contravariant vector **v** must be similarly transformed via **v**′ = //M//**v**. This important requirement is what distinguishes a contravariant vector from any other triple of physically meaningful quantities. For example, if //v// consists of the //x//, //y//, and //z//-components of velocity, then //v// is a contravariant vector: When space is stretched, rotated, or twisted, then the components of the velocity transform in the same way as space. On the other hand, for instance, a triple consisting of the length, width, and height of a rectangular box could make up the three components of an abstract vector, but this vector would not be contravariant, since rotating the box does not change the box's length, width, and height. Examples of contravariant vectors include displacement, velocity, electric field, momentum, force, and acceleration. In the language of differential geometry, the requirement that the components of a vector transform according to the same matrix of the coordinate transition is equivalent to defining a //contravariant vector// to be a tensor of contravariant rank one. Alternatively, a contravariant vector is defined to be a tangent vector, and the rules for transforming a contravariant vector follow from the chain rule. Some vectors transform like contravariant vectors, except that when they are reflected through a mirror, they flip //and// gain a minus sign. A transformation that switches right-handedness to left-handedness and vice versa like a mirror does is said to change the //orientation// of space. A vector which gains a minus sign when the orientation of space changes is called a **pseudovector** or an **axial vector**. Ordinary vectors are sometimes called **true vectors** or **polar vectors** to distinguish them from pseudovectors. Pseudovectors occur most frequently as the cross product of two ordinary vectors. One example of a pseudovector is angular velocity. Driving in a car, and looking forward, each of the wheels has an angular velocity vector pointing to the left. If the world is reflected in a mirror which switches the left and right side of the car, the //reflection// of this angular velocity vector points to the right, but the //actual// angular velocity vector of the wheel still points to the left, corresponding to the minus sign. Other examples of pseudovectors include magnetic field, torque, or more generally any cross product of two (true) vectors. This distinction between vectors and pseudovectors is often ignored, but it becomes important in studying symmetry properties. See parity (physics).