# Euclidean vector

A vector is what is needed to "carry" the point *A* to the point *B*; the Latin word *vector* means "carrier".^{[4]} It was first used by 18th century astronomers investigating planetary revolution around the Sun.^{[5]} 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,^{[6]} 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 vectors defined simply as elements of a vector space.

Vectors play an important role in physics: the velocity and acceleration of a moving object and the forces acting on it can all be described with vectors.^{[7]} Many other physical quantities can be usefully thought of as vectors. Although most of them do not represent distances (except, for example, position or displacement), their magnitude and direction can still be represented by the length and direction of an arrow. 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.^{[8]}

The concept of vector, as we know it today, evolved gradually over a period of more than 200 years. About a dozen people made significant contributions to its development.^{[9]}

In 1835, Giusto Bellavitis abstracted the basic idea when he established the concept of equipollence. Working in a Euclidean plane, he made equipollent any pair of parallel line segments of the same length and orientation. Essentially, he realized an equivalence relation on the pairs of points (bipoints) in the plane, and thus erected the first space of vectors in the plane.^{[9]}^{: 52–4 }

The term *vector* was introduced by William Rowan Hamilton as part of a quaternion, which is a sum *q* = *s* + *v* of a Real number *s* (also called *scalar*) and a 3-dimensional *vector*. Like Bellavitis, Hamilton viewed vectors as representative of classes of equipollent directed segments. As complex numbers use an imaginary unit to complement the real line, Hamilton considered the vector *v* to be the *imaginary part* of a quaternion:^{[10]}

The algebraically imaginary part, being geometrically constructed by a straight line, or radius vector, which has, in general, for each determined quaternion, a determined length and determined direction in space, may be called the vector part, or simply the vector of the quaternion.

Several other mathematicians developed vector-like systems in the middle of the nineteenth century, including Augustin Cauchy, Hermann Grassmann, August Möbius, Comte de Saint-Venant, and Matthew O'Brien. Grassmann's 1840 work *Theorie der Ebbe und Flut* (Theory of the Ebb and Flow) was the first system of spatial analysis that is similar to today's system, and had ideas corresponding to the cross product, scalar product and vector differentiation. Grassmann's work was largely neglected until the 1870s.^{[9]}

Peter Guthrie Tait carried the quaternion standard after Hamilton. His 1867 *Elementary Treatise of Quaternions* included extensive treatment of the nabla or del operator ∇.

In 1878, *Elements of Dynamic* was published by William Kingdon Clifford. Clifford simplified the quaternion study by isolating the dot product and cross product of two vectors from the complete quaternion product. This approach made vector calculations available to engineers—and others working in three dimensions and skeptical of the fourth.

Josiah Willard Gibbs, who was exposed to quaternions through James Clerk Maxwell's *Treatise on Electricity and Magnetism*, separated off their vector part for independent treatment. The first half of Gibbs's *Elements of Vector Analysis*, published in 1881, presents what is essentially the modern system of vector analysis.^{[9]}^{[6]} In 1901, Edwin Bidwell Wilson published *Vector Analysis*, adapted from Gibb's lectures, which banished any mention of quaternions in the development of vector calculus.

In physics and engineering, a vector is typically regarded as a geometric entity characterized by a magnitude and a direction. It is formally defined as a directed line segment, or arrow, in a Euclidean space.^{[11]} In pure mathematics, a vector is defined more generally as any element of a vector space. In this context, vectors are abstract entities which may or may not be characterized by a magnitude and a direction. This generalized definition implies that the above-mentioned geometric entities are a special kind of vectors, as they are elements of a special kind of vector space called Euclidean space.

This article is about vectors strictly defined as arrows in Euclidean space. When it becomes necessary to distinguish these special vectors from vectors as defined in pure mathematics, they are sometimes referred to as **geometric**, **spatial**, or **Euclidean** vectors.

The term *vector* also has generalizations to higher dimensions, and to more formal approaches with much wider applications.

A Euclidean vector is thus an equivalence class of directed segments with the same magnitude (e.g., the length of the line segment (*A*, *B*)) and same direction (e.g., the direction from A to B).^{[14]} In physics, Euclidean vectors are used to represent physical quantities that have both magnitude and direction, but are not located at a specific place, in contrast to scalars, which have no direction.^{[7]} For example, velocity, forces and acceleration are represented by vectors.

It has been proven that the two definitions of Euclidean spaces are equivalent, and that the equivalence classes under equipollence may be identified with translations.

Since the physicist's concept of force has a direction and a magnitude, it may be seen as a vector. As an example, consider a rightward force *F* of 15 newtons. If the positive axis is also directed rightward, then *F* is represented by the vector 15 N, and if positive points leftward, then the vector for *F* is −15 N. In either case, the magnitude of the vector is 15 N. Likewise, the vector representation of a displacement Δ*s* of 4 meters would be 4 m or −4 m, depending on its direction, and its magnitude would be 4 m regardless.

Vectors are fundamental in the physical sciences. They can be used to represent any quantity that has magnitude, has direction, and which adheres to the rules of vector addition. An example is 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 and follows the rules of vector addition.^{[7]} Vectors also describe many other physical quantities, such as linear displacement, displacement, linear acceleration, angular acceleration, linear 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. Examples of quantities that have magnitude and direction, but fail to follow the rules of vector addition, are angular displacement and electric current. Consequently, these are not vectors.

In Cartesian coordinates, a free vector may be thought of in terms of a corresponding bound vector, in this sense, whose initial point has the coordinates of the origin *O* = (0, 0, 0). It is then determined by the coordinates of that bound vector's terminal point. Thus the free vector represented by (1, 0, 0) is a vector of unit length—pointing along the direction of the positive *x*-axis.

This coordinate representation of free vectors allows their algebraic features to be expressed in a convenient numerical fashion. For example, the sum of the two (free) vectors (1, 2, 3) and (−2, 0, 4) is the (free) vector

In the geometrical and physical settings, it is sometimes possible to associate, in a natural way, a *length* or magnitude and a direction to vectors. In addition, the notion of direction is strictly associated with the notion of an *angle* between two vectors. If the dot product of two vectors is defined—a scalar-valued product of two vectors—then it is also possible to define a length; the dot product gives a convenient algebraic characterization of both angle (a function of the dot product between any two non-zero vectors) and length (the square root of the dot product of a vector by itself). In three dimensions, it is further possible to define the 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). In any dimension (and, in particular, higher dimensions), it's possible to define the exterior product, which (among other things) supplies an algebraic characterization of the area and orientation in space of the *n*-dimensional parallelotope defined by *n* vectors.

In a pseudo-Euclidean space, a vector's squared length can be positive, negative, or zero. An important example is Minkowski space (which is important to our understanding of special relativity).

However, it is not always possible or desirable to define the length of a vector. This more general type of spatial vector is the subject of vector spaces (for free vectors) and affine spaces (for bound vectors, as each represented by an ordered pair of "points"). One physical example comes from thermodynamics, where many quantities of interest can be considered vectors in a space with no notion of length or angle.^{[15]}

In physics, as well as mathematics, a vector is often identified with a tuple of components, or list of numbers, that act as scalar coefficients for a set of basis vectors. When the basis is transformed, for example by rotation or stretching, then the components of any vector in terms of that basis also transform in an opposite sense. The vector itself has not changed, but the basis has, so the components of the vector 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 the basis. In general, contravariant vectors are "regular vectors" with units of distance (such as a displacement), or distance times some other unit (such as velocity or acceleration); covariant vectors, on the other hand, have units of one-over-distance such as gradient. If you change units (a special case of a change of basis) from meters to millimeters, a scale factor of 1/1000, a displacement of 1 m becomes 1000 mm—a contravariant change in numerical value. In contrast, a gradient of 1 K/m becomes 0.001 K/mm—a covariant change in value (for more, see covariance and contravariance of vectors). Tensors are another type of quantity that behave in this way; a vector is one 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".

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*, and the 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, a vector perpendicular to the plane of the diagram is sometimes 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 flights of an arrow from the back.

In order to calculate with vectors, the graphical representation may be too cumbersome. Vectors in an *n*-dimensional Euclidean space can be represented as coordinate vectors in a Cartesian coordinate system. The endpoint of a vector can be identified with an ordered list of *n* real numbers (*n*-tuple). These numbers are the coordinates of the endpoint of the vector, with respect to a given Cartesian coordinate system, and are typically called the **scalar components** (or **scalar projections**) of the vector on the axes of the coordinate system.

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

In *three dimensional* Euclidean space (or **R**^{3}), vectors are identified with triples of scalar components:

These numbers are often arranged into a column vector or row vector, particularly when dealing with matrices, as follows:

Another way to represent a vector in *n*-dimensions is to introduce the standard basis vectors. For instance, in three dimensions, there are three of them:

where **a**_{1}, **a**_{2}, **a**_{3} are called the **vector components** (or **vector projections**) of **a** on the basis vectors or, equivalently, on the corresponding Cartesian axes *x*, *y*, and *z* (see figure), while *a*_{1}, *a*_{2}, *a*_{3} are the respective scalar components (or scalar projections).

The notation **e**_{i} is compatible with the index notation and the summation convention commonly used in higher level mathematics, physics, and engineering.

As explained above, a vector is often described by a set of vector components that add up to form the given vector. Typically, these components are the projections of the vector on a set of mutually perpendicular reference axes (basis vectors). The vector is said to be *decomposed* or *resolved with respect to* that set.

The decomposition or resolution^{[16]} of a vector into components is not unique, because it depends on the choice of the axes on which the vector is projected.

The choice of a basis does not affect the properties of a vector or its behaviour under transformations.

A vector can also be broken up with respect to "non-fixed" basis vectors that change their orientation as a function of time or space. For example, a vector in three-dimensional space can be decomposed with respect to two axes, respectively *normal*, and *tangent* to a surface (see figure). Moreover, the *radial* and *tangential components* of a vector relate to the *radius of rotation* of an object. The former is parallel to the radius and the latter is orthogonal to it.^{[17]}

In these cases, each of the components may be in turn decomposed with respect to a fixed coordinate system or basis set (e.g., a *global* coordinate system, or inertial reference frame).

The following section uses the Cartesian coordinate system with basis vectors

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

Two vectors are opposite if they have the same magnitude but opposite direction. So two vectors

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

The addition may be represented graphically by placing the tail of the arrow **b** at the head of the arrow **a**, and then drawing an arrow from the tail of **a** to the head of **b**. The new arrow drawn represents the vector **a** + **b**, as illustrated below:^{[7]}

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, this point 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**).

Subtraction of two vectors can be geometrically illustrated as follows: to subtract **b** from **a**, place the tails of **a** and **b** at the same point, and then draw an arrow from the head of **b** to the head of **a**. This new arrow represents the vector **(-b)** + **a**, with **(-b)** being the opposite of **b**, see drawing. And **(-b)** + **a** = **a** − **b**.

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

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**.

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").

which is a consequence of the Pythagorean theorem since the basis vectors **e**_{1}, **e**_{2}, **e**_{3} are orthogonal unit vectors.

This happens to be equal to the square root of the dot product, discussed below, of the vector with itself:

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.^{[14]} This is known as *normalizing* a vector. A unit vector is often indicated with a hat as in **â**.

To normalize a vector **a** = (*a*_{1}, *a*_{2}, *a*_{3}), scale the vector by the reciprocal of its length ‖**a**‖. That is:

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:

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 the 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

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

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**).

The cross product **a** × **b** is defined so that **a**, **b**, and **a** × **b** also becomes a right-handed system (although **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.

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).

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:

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:

The values of *p*, *q*, *r*, and *u*, *v*, *w* relate to the unit vectors in such a way that the resulting vector sum is exactly the same physical vector **a** in both cases. It is common 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 such a case it is necessary to develop a method to convert between bases so the basic vector operations such as addition and subtraction can be performed. One way to express *u*, *v*, *w* in terms of *p*, *q*, *r* is to use column matrices along with a direction cosine matrix containing the information that relates the two bases. Such an expression can be formed by substitution of the above equations to form

This matrix equation relates the scalar components of **a** in the *n* basis (*u*,*v*, and *w*) with those in the *e* basis (*p*, *q*, and *r*). Each matrix element *c*_{jk} is the direction cosine relating **n**_{j} to **e**_{k}.^{[18]} The term *direction cosine* refers to the cosine of the angle between two unit vectors, which is also equal to their dot product.^{[18]} Therefore,

By referring collectively to **e**_{1}, **e**_{2}, **e**_{3} as the *e* basis and to **n**_{1}, **n**_{2}, **n**_{3} as the *n* basis, the matrix containing all the *c*_{jk} 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*"^{[18]} (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*".

The advantage of this method is that a direction cosine matrix can usually be obtained independently by using Euler angles or a quaternion to relate the two vector bases, so the basis conversions can be performed directly, without having to work out all the dot products described above.

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.^{[18]}

With the exception of the cross and triple products, the above formulae generalise to two dimensions and higher dimensions. For example, addition generalises to two dimensions as

The cross product does not readily generalise to other dimensions, though the closely related exterior product does, whose result is a bivector. In two dimensions this is simply a pseudoscalar

A seven-dimensional cross product is similar to the cross product in that its result is a vector orthogonal to the two arguments; there is however no natural way of selecting one of the possible such products.

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 m:50 N and 1:250 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.

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 be 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.

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

Given two points **x** = (*x*_{1}, *x*_{2}, *x*_{3}), **y** = (*y*_{1}, *y*_{2}, *y*_{3}) their displacement is a vector

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

Acceleration **a** of a point is vector which is the time derivative of velocity. Its dimensions are length/time^{2}.

Force is a vector with dimensions of mass×length/time^{2} and Newton's second law is the scalar multiplication

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).