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For the square matrix section, see square matrix.
In mathematics, a matrix (plural matrices) is a rectangular table of numbers or, more generally, of elements of a ring-like algebraic structure. In this article, the entries of a matrix are real or complex numbers unless otherwise noted.
For the development and applications of matrices, see matrix theory.
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 Ai,j, or in notation of the C programming language,
We often write to define an m × n matrix A with each entry in the matrix A[i,j] called aij for all 0 ≤ i < m and 0 ≤ j < n.
is a 4×3 matrix. The element A[2,3] or a2,3 is 7.
Adding and multiplying matrices
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).
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.
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.
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.
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 -> Rm 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 : Rm -> Rk, 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 Atr (also sometimes written as AT or tA) gotten by turning rows into columns and columns into rows, i.e. Atr[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 Atr describes the transpose of the linear map with respect to the dual bases, see dual space.
We have (A + B)tr = Atr + Btr and (AB)tr = Btr * Atr.
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 In, with elements on the main diagonal set to 1 and all other elements set to 0, satisfies MIn=M and InN=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 = In ( = BA).
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−λIn is not invertible, which happens if and only if pA(λ) = 0. Here pA(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 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.
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 to the main diagonal (from the upper left to the lower right) are equal, that is, ai,j=aj,i.
- Hermitian (or self-adjoint) matrices are such that elements symmetric to the diagonal are each others complex conjugates, that is, ai,j=a*j,i, where the superscript '*' signifies complex conjugation.
- Toeplitz matrices have common elements on their diagonals, that is, ai,j=ai+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 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 ring of the left R-module Rn.
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 articles for real or complex matrices remain correct for matrices over an arbitrary field.
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.
Olga Taussky Todd (1906-1995) started to use matrix theory when investigating an aerodynamic phenomenon called flutter, during WWII.
A more advanced article on matrices is matrix theory.
- Matrix name and history: very brief overview
- WIMS Matrix Calculator computes determinant, rank, inverse etc. online.
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