# Matrix (mathematics)

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In mathematics, a **matrix** (plural **matrices**) is a rectangular table of numbers or, more generally, a table consisting of abstract quantities that can be added and multiplied. Matrices are used to describe linear equations, keep track of the coefficients of linear transformations and to record data that depend on two parameters. Matrices can be added, multiplied, and decomposed in various ways, making them a key concept in linear algebra and matrix theory.

In this article, the entries of a matrix are real or complex numbers unless otherwise noted.

## Definitions and notations

The horizontal lines in a matrix are called **rows** and the vertical lines are called **columns**. A matrix with *m* rows and *n* columns is called an *m*-by-*n* matrix (written *m*×*n*) and *m* and *n* are called its **dimensions**. The dimensions of a matrix are always given with the number of rows first, then the number of columns.

The entry of a matrix *A* that lies in the *i* -th row and the *j*-th column is called the *i,j* entry or (*i*,*j*)-th entry of *A*. This is written as *A*_{i,j} or *A*[*i,j*]. The row is always noted first, then the column.

We often write to define an *m* × *n* matrix *A* with each entry in the matrix *A*[*i,j*] called *a*_{ij} for all 1 ≤ *i* ≤ *m* and 1 ≤ *j* ≤ *n*. However, the convention that the indices *i* and *j* start at 1 is not universal: some programming languages start at zero, in which case we have 0 ≤ *i* ≤ *m* − 1 and 0 ≤ *j* ≤ *n* − 1.

A matrix where one of the dimensions equals one is often called a *vector*, and interpreted as an element of real coordinate space. A 1 × *n* matrix (one row and *n* columns) is called a row vector, and an *m* × 1 matrix (one column and *m* rows) is called a column vector.

## Example

The matrix

is a 4×3 matrix. The element *A*[2,3] or *a*_{2,3} is 7.

The matrix

is a 1×9 matrix, or 9-element row vector.

## Adding and multiplying matrices

### Sum

Given *m*-by-*n* matrices *A* and *B*, their **sum** *A + B* is the *m*-by-*n* matrix computed by adding corresponding elements (i.e. (*A + B*)[*i, j*] = *A*[*i, j*] + *B*[*i, j*] ). For example:

Another, much less often used notion of matrix addition is the direct sum.

### Scalar multiplication

Given a matrix *A* and a number *c*, the ** scalar multiplication** *cA* is computed by multiplying the scalar *c* by every element of *A* (i.e. (*cA*)[*i*, *j*] = *cA*[*i*, *j*] ). For example:

### Matrix multiplication

**Multiplication** of two matrices is well-defined only if the number of columns of the left matrix is the same as the number of rows of the right matrix. If *A* is an *m*-by-*n* matrix and *B* is an *n*-by-*p* matrix, then their **matrix product** *AB* is the *m*-by-*p* matrix (*m* rows, *p* columns) given by:

for each pair *i* and *j*.

For example:

These two operations turn the set M(*m*, *n*, **R**) of all *m*-by-*n* matrices with real entries into a real vector space of dimension *mn*.

Matrix multiplication has the following properties:

- (
*AB*)*C*=*A*(*BC*) for all*k*-by-*m*matrices*A*,*m*-by-*n*matrices*B*and*n*-by-*p*matrices*C*("associativity"). - (
*A + B*)*C*=*AC*+*BC*for all*m*-by-*n*matrices*A*and*B*and*n*-by-*k*matrices*C*("right distributivity"). *C*(*A + B*) =*CA*+*CB*for all*m*-by-*n*matrices*A*and*B*and*k*-by-*m*matrices*C*("left distributivity").

It is important to note that commutativity does *not* generally hold; that is, given matrices *A* and *B* and their product defined, then generally *AB* ≠ *BA*.

## Linear transformations, ranks and transpose

Matrices can conveniently represent linear transformations because matrix multiplication neatly corresponds to the composition of maps, as will be described next. This same property makes them powerful data structures in high-level programming languages.

Here and in the sequel we identify **R**^{n} with the set of "columns" or *n*-by-1 matrices. For every linear map *f* : **R**^{n} → **R**^{m} there exists a unique *m*-by-*n* matrix *A* such that *f*(*x*) = *Ax* for all *x* in **R**^{n}. We say that the matrix *A* "represents" the linear map *f*. Now if the *k*-by-*m* matrix *B* represents another linear map *g* : **R**^{m} → **R**^{k}, then the linear map *g* o *f* is represented by *BA*. This follows from the above-mentioned associativity of matrix multiplication.

More generally, a linear map from an *n*-dimensional vector space to an *m*-dimensional vector space is represented by an *m*-by-*n* matrix, provided that bases have been chosen for each.

The rank of a matrix *A* is the dimension of the image of the linear map represented by *A*; this is the same as the dimension of the space generated by the rows of *A*, and also the same as the dimension of the space generated by the columns of *A*.

The transpose of an *m*-by-*n* matrix *A* is the *n*-by-*m* matrix *A*^{tr} (also sometimes written as *A*^{T} or ^{t}*A*) formed by turning rows into columns and columns into rows, i.e. *A*^{tr}[*i*, *j*] = *A*[*j*, *i*] for all indices *i* and *j*. If *A* describes a linear map with respect to two bases, then the matrix *A*^{tr} describes the transpose of the linear map with respect to the dual bases, see dual space.

We have (*A + B*)^{tr} = *A*^{tr} + *B*^{tr} and (*AB*)^{tr} = *B*^{tr} *A*^{tr}.

## Square matrices and related definitions

A **square matrix** is a matrix which has the same number of rows and columns. The set of all square *n*-by-*n* matrices, together with matrix addition and matrix multiplication is a ring. Unless *n* = 1, this ring is not commutative.

M(*n*, **R**), the ring of real square matrices, is a real unitary associative algebra. M(*n*, **C**), the ring of complex square matrices, is a complex associative algebra.

The **unit matrix** or ** identity matrix** *I _{n}*, with elements on the main diagonal set to 1 and all other elements set to 0, satisfies

*MI*and

_{n}=M*I*for any

_{n}N=N*m*-by-

*n*matrix

*M*and

*n*-by-

*k*matrix

*N*. For example, if

*n*= 3:

The identity matrix is the identity element in the ring of square matrices.

Invertible elements in this ring are called ** invertible matrices** or **non-singular matrices**. An *n* by *n* matrix *A* is invertible if and only if there exists a matrix *B* such that

*AB*= I_{n}( =*BA*).

In this case, *B* is the ** inverse matrix** of *A*, denoted by *A*^{−1}. The set of all invertible *n*-by-*n* matrices forms a group (specifically a Lie group) under matrix multiplication, the general linear group.

If λ is a number and **v** is a non-zero vector such that *A***v** = λ**v**, then we call **v** an eigenvector of *A* and λ the associated eigenvalue. (Eigen means "own" in German.) The number λ is an eigenvalue of *A* if and only if *A*−λ*I*_{n} is not invertible, which happens if and only if *p*_{A}(λ) = 0. Here *p*_{A}(*x*) is the characteristic polynomial of *A*. This is a polynomial of degree *n* and has therefore *n* complex roots (counting multiple roots according to their multiplicity). In this sense, every square matrix has *n* complex eigenvalues.

The determinant of a square matrix *A* is the product of its *n* eigenvalues, but it can also be defined by the * Leibniz formula*. Invertible matrices are precisely those matrices with nonzero determinant.

The Gaussian elimination algorithm is of central importance: it can be used to compute determinants, ranks and inverses of matrices and to solve systems of linear equations.

The trace of a square matrix is the sum of its diagonal entries, which equals the sum of its *n* eigenvalues.

Matrix exponential is defined for square matrices, using power series.

## Special types of matrices

In many areas in mathematics, matrices with certain structure arise. A few important examples are

- Symmetric matrices are such that elements symmetric about the
*main diagonal*(from the upper left to the lower right) are equal, that is, a_{i,j}=a_{j,i}. - Skew-symmetric matrices are such that elements symmetric about the
*main diagonal*are the negative of each other, that is, a_{i,j}= - a_{j,i}. In a skew-symmetric matrix, all diagonal elements are zero, that is, a_{i,i}=0. - Hermitian (or self-adjoint) matrices are such that elements symmetric about the diagonal are each others complex conjugates, that is, a
_{i,j}=a^{*}_{j,i}, where the superscript '*' signifies complex conjugation. - Toeplitz matrices have common elements on their diagonals, that is, a
_{i,j}=a_{i+1,j+1}. - Stochastic matrices are square matrices whose columns are probability vectors; they are used to define Markov chains.

For a more extensive list see list of matrices.

## Matrices in abstract algebra

If we start with a ring *R*, we can consider the set M(*m*,*n*, *R*) of all *m* by *n* matrices with entries in *R*. Addition and multiplication of these matrices can be defined as in the case of real or complex matrices (see above). The set M(*n*, *R*) of all square *n* by *n* matrices over *R* is a ring in its own right, isomorphic to the endomorphism ring of the left *R*- module *R*^{n}.

Similarly, if the entries are taken from a semiring *S*, matrix addition and multiplication can still be defined as usual. The set of all square *n*×*n* matrices over *S* is itself a semiring. Note that fast matrix multiplication algorithms such as the Strassen algorithm generally only apply to matrices over rings and will not work for matrices over semirings that are not rings.

If *R* is a commutative ring, then M(*n*, *R*) is a unitary associative algebra over *R*. It is then also meaningful to define the determinant of square matrices using the * Leibniz formula*; a matrix is invertible if and only if its determinant is invertible in *R*.

All statements mentioned in this article for real or complex matrices remain correct for matrices over an arbitrary field.

Matrices over a polynomial ring are important in the study of control theory.

## History

The study of matrices is quite old. Latin squares and magic squares have been studied since prehistoric times.

Matrices have a long history of application in solving linear equations. Leibniz, one of the two founders of calculus, developed the theory of determinants in 1693. Cramer developed the theory further, presenting Cramer's rule in 1750. Carl Friedrich Gauss and Wilhelm Jordan developed Gauss-Jordan elimination in the 1800s.

The term "matrix" was first coined in 1848 by J. J. Sylvester. Cayley, Hamilton, Grassmann, Frobenius and von Neumann are among the famous mathematicians who have worked on matrix theory.

Olga Taussky Todd (1906-1995) started to use matrix theory when investigating an aerodynamic phenomenon called fluttering or aeroelasticity, during WWII.

## Applications

### Transportation

If one is given a list of cities (or destinations, nodes, etc.) and is told that there are flights (or roads, connections, etc.) from city *a* to city *b*, then one can build a square matrix with the cities indexing each side of the matrix. Each entry *M*_{a,b} is set to 1 if there is a connection from *a* to *b*; it is 0 otherwise. If there is a reverse connection, going from *b* to *a*, then also *M*_{b,a} = 1. In many instances the connection *a* to *b* might not be bidirectional, i.e. *M*_{a,b} = 1 does not necessarily imply that *M*_{b,a} = 1.

By multiplying the matrix *M* by itself one obtains *M*^{2}. The matrix *M*^{2} will indicate if you can go from *a* to *b* via a third city. If (*M*^{2})_{a,b} = 1, then there exists one third city *c* which acts as a layover, that is, you can go from *a* to *c* and then from *c* to *b*. If (*M*^{2})_{a,b} = *n*, then there are *n* such layovers.

### Encryption

*See Matrix encryption*

Matrices can be used to encrypt numerical data. Encryption is done by multiplying the data matrix with a key matrix. Decryption is done simply by multiplying the encrypted matrix with the inverse of the key.

### Computer Graphics

4x4 transformation matrices are commonly used in computer graphics. The upper left 3x3 portion of a transformation matrix is composed of the new X, Y, and Z axes of the post-transformation coordinate space.