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), direction and sense that is orientation along the given direction.^[3] 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,^[4] and denoted by

$\overrightarrow{AB}.$

The magnitude of the vector is the length of the segment and the direction characterizes the displacement of B relative to A: how much one should move the point A to "carry" it to the point B.^[5]

Many algebraic operations on real numbers have close analogues for vectors. Vectors can be added, subtracted, multiplied by a number, and flipped around so that the direction is reversed. These operations obey the familiar algebraic laws: commutativity, associativity, distributivity. The sum of two vectors with the same initial point can be found geometrically using the parallelogram law. Multiplication by a positive number, commonly called a scalar in this context, amounts to changing the magnitude of vector, that is, stretching or compressing it while keeping its direction; multiplication by -1 preserves the magnitude of the vector but reverses its direction.

Cartesian coordinates provide a systematic way of describing vectors and operations on them. A vector becomes a tuple of real numbers, its scalar components. Addition of vectors and multiplication of a vector by a scalar are simply done component by component, see coordinate vector.

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.

1 Overview
2 Representation of a vector
3 Basic properties
4 Derivative of a vector function
5 Vectors in physics
6 Vector components
7 Vectors as directional derivatives
8 Vectors, pseudovectors, and transformations
9 See also
10 Notes
11 References
12 External links

Overview

In this context, a vector is a geometric entity characterized by a magnitude (in mathematics a number, in physics a number times a unit) and a direction, often represented graphically by an arrow. When it becomes necessary to distinguish it from vectors as defined elsewhere, this is sometimes referred to as a geometric, spatial, or Euclidean vector.

When a vector is thought of as an arrow in Euclidean space, it 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 $\overrightarrow{AB}$ and $\overrightarrow{A'B'}$ 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 larger dimensions and to more formal approaches with much wider applications. Such generalizations are found in other articles. See the See also links and links within the text below.

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 $\overrightarrow{AB}$ 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

$(1,\, 2,\, 3) + (-2,\, 0,\, 4)=(1-2,\, 2+0,\, 3+4)=(-1,\, 2,\, 7).\,$

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 $\vec{a}$ 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 $\overrightarrow{AB}$ or AB. The hat symbol (^) is typically used to denote unit vectors (vectors with unit length), as in $\boldsymbol{\hat{a}}$ .

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.

Notation for vectors in or out of a plane.svg

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 an arrow front 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

$\mathbf{a} = (2,3).$

The notion that the tail of the vector coincides with the origin is implicit and easily understood. Thus, the more explicit notation $\overrightarrow{OA}$ is usually not deemed necessary and very rarely used.

In three dimensional Euclidean space (or R³), vectors are identified with triples of numbers corresponding to the Cartesian coordinates of the endpoint (a,b,c):

$\mathbf{a} = (a, b, c).$

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

$\mathbf{a} = \begin{bmatrix} a\\ b\\ c\\ \end{bmatrix}$

$\mathbf{a} = [ a\ b\ c ].$

Another way to express a vector in three dimensions is to introduce the three standard basis vectors:

${\mathbf e}_1 = (1,0,0),\ {\mathbf e}_2 = (0,1,0),\ {\mathbf e}_3 = (0,0,1).$

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 R³ can be expressed in the form:

$(a,b,c) = a(1,0,0) + b(0,1,0) + c(0,0,1) = a{\mathbf e}_1 + b{\mathbf e}_2 + c{\mathbf e}_3.$

In introductory physics classes, these three special vectors are often instead denoted $\boldsymbol{i},\boldsymbol{j},\boldsymbol{k}$ (or $\boldsymbol{\hat{x}}, \boldsymbol{\hat{y}}, \boldsymbol{\hat{z}}$ ), but such notation clashes with the index notation and the summation convention commonly used in higher level mathematics, physics, and engineering.

The use of Cartesian versors such as $\boldsymbol{\hat{x}}, \boldsymbol{\hat{y}}, \boldsymbol{\hat{z}}$ as a basis in which to represent a vector is not mandated. Vectors can also be expressed in terms of cylindrical unit vectors $\boldsymbol{\hat{r}}, \boldsymbol{\hat{\theta}}, \boldsymbol{\hat{z}}$ or spherical unit vectors $\boldsymbol{\hat{r}}, \boldsymbol{\hat{\theta}}, \boldsymbol{\hat{\phi}}$ . 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

${\mathbf e}_1 = (1,0,0),\ {\mathbf e}_2 = (0,1,0),\ {\mathbf e}_3 = (0,0,1)$

and assume that all vectors have the origin as a common base point. A vector a will be written as

${\mathbf a} = a_1{\mathbf e}_1 + a_2{\mathbf e}_2 + a_3{\mathbf e}_3.$

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

${\mathbf a} = a_1{\mathbf e}_1 + a_2{\mathbf e}_2 + a_3{\mathbf e}_3$

and

${\mathbf b} = b_1{\mathbf e}_1 + b_2{\mathbf e}_2 + b_3{\mathbf e}_3$

are equal if

$a_1 = b_1,\quad a_2=b_2,\quad a_3=b_3.\,$

Addition and subtraction

The sum of a and b is

$\mathbf{a}+\mathbf{b} =(a_1+b_1)\mathbf{e_1} +(a_2+b_2)\mathbf{e_2} +(a_3+b_3)\mathbf{e_3}.$

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

$\mathbf{a}-\mathbf{b} =(a_1-b_1)\mathbf{e_1} +(a_2-b_2)\mathbf{e_2} +(a_3-b_3)\mathbf{e_3}.$

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

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

$r\mathbf{a}=(ra_1)\mathbf{e_1} +(ra_2)\mathbf{e_2} +(ra_3)\mathbf{e_3}.$

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

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:

The scalar multiplications 2a and −a of a vector a

Scalar multiplication is distributive over vector addition in the following sense: r(a + b) = ra + rb 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

$\left\|\mathbf{a}\right\|=\sqrt{{a_1}^2+{a_2}^2+{a_3}^2}$

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:

$\left\|\mathbf{a}\right\|=\sqrt{\mathbf{a}\cdot\mathbf{a}}.$

Dot product

Main article: 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:

$\mathbf{a}\cdot\mathbf{b} =\left\|\mathbf{a}\right\|\left\|\mathbf{b}\right\|\cos\theta$

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

$\mathbf{a} \cdot \mathbf{b} = a_1 b_1 + a_2 b_2 + a_3 b_3.$

Unit vector

Main article: 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 it 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 = [a₁, a₂, a₃], scale the vector by the reciprocal of its length ||a||. That is:

$\mathbf{\hat{a}} = \frac{\mathbf{a}}{\left\|\mathbf{a}\right\|} = \frac{a_1}{\left\|\mathbf{a}\right\|}\mathbf{e_1} + \frac{a_2}{\left\|\mathbf{a}\right\|}\mathbf{e_2} + \frac{a_3}{\left\|\mathbf{a}\right\|}\mathbf{e_3}$

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 $\vec{0}$ , 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 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

$\mathbf{a}\times\mathbf{b} =\left\|\mathbf{a}\right\|\left\|\mathbf{b}\right\|\sin(\theta)\,\mathbf{n}$

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

${\mathbf a}\times{\mathbf b} = (a_2 b_3 - a_3 b_2) {\mathbf e}_1 + (a_3 b_1 - a_1 b_3) {\mathbf e}_2 + (a_1 b_2 - a_2 b_1) {\mathbf e}_3.$

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: 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:

$(\mathbf{a}\ \mathbf{b}\ \mathbf{c}) =\mathbf{a}\cdot(\mathbf{b}\times\mathbf{c}).$

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

$(\mathbf{a}\ \mathbf{b}\ \mathbf{c})=\left|\begin{pmatrix} a_1 & a_2 & a_3 \\ b_1 & b_2 & b_3 \\ c_1 & c_2 & c_3 \\ \end{pmatrix}\right|.$

The scalar triple product is linear in all three entries and anti-symmetric in the following sense:

$(\mathbf{a}\ \mathbf{b}\ \mathbf{c}) = (\mathbf{c}\ \mathbf{a}\ \mathbf{b}) = (\mathbf{b}\ \mathbf{c}\ \mathbf{a})= -(\mathbf{a}\ \mathbf{c}\ \mathbf{b}) = -(\mathbf{b}\ \mathbf{a}\ \mathbf{c}) = -(\mathbf{c}\ \mathbf{b}\ \mathbf{a}).$

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,

$\mathbf{a} = a_1\mathbf{e}_1 + a_2\mathbf{e}_2 + a_3\mathbf{e}_3 = u\mathbf{n}_1 + v\mathbf{n}_2 + w\mathbf{n}_3$

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

$\begin{bmatrix} u \\ v \\ w \\ \end{bmatrix} = \begin{bmatrix} c_{11} & c_{12} & c_{13} \\ c_{21} & c_{22} & c_{23} \\ c_{31} & c_{32} & c_{33} \end{bmatrix} \begin{bmatrix} a_1 \\ a_2 \\ a_3 \end{bmatrix}$

where each matrix element c_ij is the direction cosine relating n_i to e_j.^[6] The term direction cosine refers 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 c_ij 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 bases

The basis for the Cartesian coordinate system used above is an Orthonormal basis where the basic vectors are orthogonal and of unit length. The above results also apply for other orthonormal bases, such as cylindrical with unit vectors $\boldsymbol{\hat{r}}, \boldsymbol{\hat{\theta}}, \boldsymbol{\hat{z}}$ , or spherical with unit vectors $\boldsymbol{\hat{r}}, \boldsymbol{\hat{\theta}}, \boldsymbol{\hat{\phi}}$ .

Addition, subtraction, and scalar multiplication also generalise naturally if the basis vectors are linearly independent. The dot product can also be defined for such bases; however, its interpretation as a length does not follow.

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

$(a_1{\mathbf e}_1 + a_2{\mathbf e}_2)+(b_1{\mathbf e}_1 + b_2{\mathbf e}_2) = (a_1+b_1){\mathbf e}_1 + (a_2+b_2){\mathbf e}_2$

and in four dimension

$\begin{align}(a_1{\mathbf e}_1 + a_2{\mathbf e}_2 + a_3{\mathbf e}_3 + a_4{\mathbf e}_4)& + (b_1{\mathbf e}_1 + b_2{\mathbf e}_2 + b_3{\mathbf e}_3 + b_4{\mathbf e}_4)\\ &= (a_1+b_1){\mathbf e}_1 + (a_2+b_2){\mathbf e}_2 + (a_3+b_3){\mathbf e}_3 + (a_4+b_4){\mathbf e}_4.\end{align}$

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

$(a_1{\mathbf e}_1 + a_2{\mathbf e}_2)\wedge(b_1{\mathbf e}_1 + b_2{\mathbf e}_2) = a_1 b_2 - b_2 a_1.$

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.

Derivative of a vector function

If a vector is a function of one or more scalar variables, the vector function can be differentiated with respect to those variables. The result will be a vector function with the same number of dimensions as the original. For example,

$\frac{\mathrm d\bold{v}(t)}{\mathrm d t}=\bold{a}(t)$

where v is the velocity and a is the acceleration.

Partial derivative

The partial derivative of a vector function a with respect to a scalar variable q is defined as^[7]

$\frac{\partial\mathbf{a}}{\partial q} = \sum_{i=1}^{3}\frac{\partial a_i}{\partial q}\mathbf{e}_i$

where a_i is the scalar component of a in the direction of e_i. It is also called the direction cosine of a and e_i or their dot product. The vectors e₁,e₂,e₃ form an orthonormal basis fixed in the reference frame in which the derivative is being taken.

Ordinary derivative

If a is regarded as a vector function of a single scalar variable, such as time t, then the equation above reduces to the first ordinary time derivative of a with respect to t,^[7]

$\frac{\mathrm d\mathbf{a}}{\mathrm d t} = \sum_{i=1}^{3}\frac{\mathrm d a_i}{\mathrm d t}\mathbf{e}_i.$

Total derivative

If the vector a is a function of a number n of scalar variables q_r (r = 1...n), and each q_r is only a function of time t, then the ordinary derivative of a with respect to t can be expressed, in a form known as the total derivative, as^[7]

$\frac{\mathrm d\mathbf a }{\mathrm d t} = \sum_{r=1}^{n}\frac{\partial \mathbf a}{\partial q_r} \frac{\mathrm d q_r}{\mathrm d t} + \frac{\partial \mathbf a}{\partial t}.$

Some authors prefer to use capital D to indicate the total derivative operator, as in D/Dt. The total derivative differs from the partial time derivative in that the total derivative accounts for changes in a due to the time variance of the variables q_r.

Derivative of a vector function with nonconstant bases

The above formulas for the derivative of a vector function rely on the assumption that the basis vectors e₁,e₂,e₃ are constant, and therefore have a derivative of zero. This often holds true for problems dealing with vector fields in a fixed coordinate system, or for simple problems in physics. However, many complex problems involve the derivative of a vector function in multiple moving reference frames, which means that the basis vectors will not necessarily be constant. In such a case where the basis vectors e₁,e₂,e₃ are fixed in reference frame E, but not in reference frame N, the more general formula for the ordinary time derivative of a vector in reference frame N is^[7]

$\frac{{}^\mathrm{N}\mathrm d\mathbf{a}}{\mathrm d t} = \sum_{i=1}^{3}\frac{\mathrm d a_i}{\mathrm d t}\mathbf{e}_i + \sum_{i=1}^{3}a_i\frac{{}^\mathrm{N}\mathrm d \mathbf{e}_i}{\mathrm d t}$

where the superscript N to the left of the derivative operator indicates the reference frame in which the derivative is taken. As shown previously, the first term on the right hand side is equal to the derivative of a in the reference frame where e₁,e₂,e₃ are constant, reference frame E. It also can be shown that the second term on the right hand side is equal to the relative angular velocity of the two reference frames cross multiplied with the vector a itself.^[7] Thus, after substitution, the formula relating the derivative of a vector function in two reference frames is^[7]

$\frac{{}^\mathrm N\mathrm d\mathbf a}{\mathrm dt} = \frac{{}^\mathrm E\mathrm d\mathbf a }{\mathrm dt} + {}^\mathrm N \mathbf \omega^\mathrm E \times \mathbf a$

where ^Nω^E is the angular velocity of the reference frame E relative to the reference frame N.

One common example where this formula is used is to find the velocity of a space-borne object, such as a rocket, in the inertial reference frame using measurements of the rocket's velocity relative to the ground. The velocity ^Nv^R in inertial reference frame N of a rocket R located at position r^R can be found using the formula

${}^\mathrm N \mathbf v^\mathrm R = {}^\mathrm E \mathbf v^\mathrm R + {}^\mathrm N \mathbf \omega^\mathrm E \times \mathbf r^\mathrm R.$

where ^Ev^R is the velocity vector of the rocket as measured from a reference frame E that is fixed to the Earth, and ^Nω^E is the angular velocity of the Earth relative to the inertial frame N. Since velocity is the derivative of position, ^Nv^R and ^Ev^R are the derivatives of r^R in reference frames N and E, respectively.

Derivative and vector multiplication

The derivative of the products of vector functions behaves similarly to the derivative of the products of scalar functions. Specifically, in the case of scalar multiplication of a vector, if p is a scalar variable function of q,^[7]

$\frac{\partial}{\partial q}(p\mathbf a) = \frac{\partial p}{\partial q}\mathbf a + p\frac{\partial \mathbf a}{\partial q}.$

In the case of dot multiplication, for two vectors a and b that are both functions of q,^[7]

$\frac{\partial}{\partial q}(\mathbf a \cdot \mathbf b) = \frac{\partial \mathbf a }{\partial q} \cdot \mathbf b + \mathbf a \cdot \frac{\partial \mathbf b}{\partial q}.$

Similarly, the derivative of the cross product of two vector functions is^[7]

$\frac{\partial}{\partial q}(\mathbf a \times \mathbf b) = \frac{\partial \mathbf a }{\partial q} \times \mathbf b + \mathbf a \times \frac{\partial \mathbf b}{\partial q}.$

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.

Position, velocity and acceleration

The position of a point p=(p₁, p₂, p₃) in three dimensional space can be represented as a position vector whose base point is the origin

${\mathbf p} = p_1 {\mathbf e}_1 + p_2{\mathbf e}_2 + p_3{\mathbf e}_3.$

The position vectors has dimensions of length.

Given two points p=(p₁, p₂, p₃), q=(q₁, q₂, q₃) their displacement is a vector

${\mathbf q}-{\mathbf p}=(q_1-p_1){\mathbf e}_1 + (q_2-p_2){\mathbf e}_2 + (q_3-p_3){\mathbf e}_3.$

which specifies the position of q relative to p. The length of this vector gives the straight line distance from p to q. 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

${\mathbf p}_t= t {\mathbf v} + {\mathbf p}_0,$

where p₀ 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

${\mathbf F} = m{\mathbf a}$

Work is the dot product of force and displacement

$E = {\mathbf F} \cdot ({\mathbf p}_2 - {\mathbf p}_1).$

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 $f(x^\alpha)$ and a curve $x^\alpha (\tau)$ . Then the directional derivative of $f$ is a scalar defined as

$\frac{df}{d\tau} = \sum_{\alpha=1}^n \frac{dx^\alpha}{d\tau}\frac{\partial f}{\partial x^\alpha}.$

where the index $\alpha$ is summed over the appropriate number of dimensions (for example, from 1 to 3 in 3-dimensional Euclidian space, from 0 to 3 in 4-dimensional spacetime, etc.). Then consider a vector tangent to $x^\alpha (\tau)$ :

$t^\alpha = \frac{dx^\alpha}{d\tau}.$

The directional derivative can be rewritten in differential form (without a given function $f$ ) as

$\frac{d}{d\tau} = \sum_\alpha t^\alpha\frac{\partial}{\partial x^\alpha}.$

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

$\mathbf{a} \equiv a^\alpha \frac{\partial}{\partial x^\alpha}.$

Vectors, pseudovectors, 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′ = Mx, then a contravariant vector v must be similarly transformed via v′ = Mv. 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).

Notes

↑ Ivanov 2001
↑ Heinbockel 2001
↑ B. B. Muvdi, Amir Wadi Al-Khafaji, J. W. McNabb, Statics for Engineers, Springer, 1997, page 33, ISBN 0387947795
↑ Ito 1993; Pedoe 1988
↑ Indeed in Latin the word vector means "one who carries"; Latin veho = "I carry". For historical development of the word vector, see "vector n.". Oxford English Dictionary. Oxford University Press. 2nd ed. 1989. and Jeff Miller. "Earliest Known Uses of Some of the Words of Mathematics". Retrieved on 2007-05-25..
↑ ^6.0 ^6.1 ^6.2 ^6.3 Kane, Thomas R.; Levinson, David A. (1996). "1-6 Direction Cosines". Dynamics Online. Sunnyvale, California: OnLine Dynamics, Inc.. pp. 20–22.
↑ ^7.0 ^7.1 ^7.2 ^7.3 ^7.4 ^7.5 ^7.6 ^7.7 ^7.8 Kane, Thomas R.; Levinson, David A. (1996). "1-9 Differentiation of Vector Functions". Dynamics Online. Sunnyvale, California: OnLine Dynamics, Inc.. pp. 29–37.

References

Mathematical treatments

Apostol, T. (1967). Calculus, Vol. 1: One-Variable Calculus with an Introduction to Linear Algebra. John Wiley and Sons. ISBN 978-0471000051.
Apostol, T. (1969). Calculus, Vol. 2: Multi-Variable Calculus and Linear Algebra with Applications. John Wiley and Sons. ISBN 978-0471000075.
Heinbockel, J. H. (2001), Introduction to Tensor Calculus and Continuum Mechanics, Trafford Publishing, ISBN 1553691334, http://www.math.odu.edu/~jhh/counter2.html
Ito, Kiyosi (1993), Encyclopedic Dictionary of Mathematics (2nd ed.), MIT Press, ISBN 978-0-262-59020-4
Ivanov, A.B. (2001), "Vector, geometric", in Hazewinkel, Michiel, Encyclopaedia of Mathematics, Kluwer Academic Publishers, ISBN 978-1556080104
Pedoe, D. (1988). Geometry: A comprehensive course. Dover. ISBN 0-486-65812-0. .

Physical treatments

Aris, R. (1990). Vectors, Tensors and the Basic Equations of Fluid Mechanics. Dover. ISBN 978-0486661100.
Feynman, R., Leighton, R., and Sands, M. (2005). "Chapter 11". The Feynman Lectures on Physics, Volume I (2nd ed ed.). Addison Wesley. ISBN 978-0805390469.

External links

Online vector identities (PDF)
Introducing Vectors A conceptual introduction (applied mathematics)

Topics related to linear algebra

Scalar · Vector · Vector space · Vector projection · Linear span · Linear map · Linear projection · Linear independence · Linear combination · Basis · Column space · Row space · Dual space · Orthogonality · Rank · Minor · Kernel (matrix) · Eigenvalue, eigenvector and eigenspace · Least squares regressions · Outer product · Inner product space · Cross product · Dot product · Transpose · Gram–Schmidt process · Matrix decomposition