Matrix (mathematics) History Definitions And Notations

In mathematics, a matrix (plural matrices) is a rectangular table of numbers or, more generally, of elements of a fixed ring. In this article, if unspecified, the entries of a matrix are always real or complex numbers.

1 History

2 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 (or m×n matrix) and m and n are called its dimensions.

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, or in notation of the C programming language, A[i][j].

Matrix storage uses two conventions: row major and column major ordering. The former means that the matrix is stored such that row elements are packed together contiguously, the latter means that the matrix is stored such that column elements are packed together contiguously.

3 Examples

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

For a more extensive list see list of matricesListed below are some important classes of matrices used in mathematics: (0,1)-matrix or binary matrix a matrix with all elements either 0 or 1. Adjacency matrix a symmetric (0,1)-matrix Anti-Hermitian matrix another name for a skew-Hermitian matrix''..

4 Matrices with entries in arbitrary rings

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 below ). 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 ringIn abstract algebra, one associates to certain objects a ring, the object's endomorphism ring which encodes several internal properties of the object. We will start with the example of abelian groups. Suppose A is an abelian group. As the name suggests, t of the left R- module Rⁿ.

If R is commutative, 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 articles 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.

5 Adding and multiplying matrices

5.1 Sum

If two m-by-n matrices A and B are given, we may define their sum A + B as 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 can be found at Direct sum (Matrix).

5.2 Scalar multiplication

If a matrix A and a number c are given, we may define the scalar multiplication cA by (cA)[i, j] = cA[i, 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.

5.3 Multiplication

Main article: Matrix multiplication

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

(AB)[i, j] = A[i, 1] * B[1, j] + A[i, 2] * B[2, j] + ... + A[i, n] * B[n, j] for each pair i and j.

For instance

This 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 ("distributivity").
C(A + B) = CA + CB for all m-by-n matrices A and B and k-by-m matrices C ("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.

Matrices are said to anticommute if AB = -BA they are very important in representations of Lie algebras and in Representations of Clifford algebras

6 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.

Here and in the sequel we identify Rⁿ with the set of "rows" or n-by-1 matrices. For every linear map f : Rⁿ -> R^m there exists a unique m-by-n matrix A such that f(x) = Ax for all x in Rⁿ. 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.

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 ^tA) gotten 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.

7 Square matrices and related definitions

A square matrix is a matrix which has the same number of rows as 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_n=M and I_nN=N for any 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 Av = λv, 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−λI_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 Gauss-Jordan 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.

Every orthogonal matrix is a square matrix.

8 See also

wikibooks:abstract algebra:matrices

9 External link

WIMS Matrix Calculator computes determinant, rank, inverse etc. online.

<Hide>

Home > Matrix (mathematics)