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: ''For the square matrix section, see square matrix''.
In mathematics, a 'matrix' (plural 'matrices') is a rectangular table of ''elements'' (or ''entries''), which may be numbers or, more generally, any 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 multiple 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.

Contents

Definitions and notations

Example

Adding and multiplying matrices

Sum

Scalar multiplication

Matrix multiplication

Linear transformations, ranks and transpose

Square matrices and related definitions

Special types of matrices

Matrices in abstract algebra

History

Applications

Encryption

Computer graphics

Further reading

See also

References

External links

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 $m\; imes\; n$) 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. It is commonly said that an $m$-by-$n$ matrix has an 'order' of $m\; imes\; n$ (order meaning size). Two matrices of the same order whose corresponding entries are equivalent are considered equal.
Almost always capital letters denote matrices with the corresponding lower case letter with two indices representing the entries. For example the entry of a matrix $A$ that lies in the $i$-th row and the $j$-th column is written as $a\_\{i,j\}$ and called the $i,j$ entry or $(i,j)$-th entry of $A$. Alternative notations for that entry are $A[i,j]$ or $A\_\{i,j\}$. The row is always noted first, then the column.
We often write $A:=(a\_\{i,j\})\_\{i=1,ldots,m;j=1,ldots,n\}$ or $A:=(a\_\{i,j\})\_\{m\; imes\; n\}$ to define an $m\; imes\; n$ matrix $A$. In this case the entries $a\_\{i,j\}$ are defined separately for all integers $1le\; i\; le\; m$ and $1le\; j\; le\; n$. In some programming languages the numbering of rows and colums starts at zero. Texts, which make use of such a language extensively, frequently follow that convention, so we have $0le\; i\; le\; m-1$ and $0le\; j\; le\; n-1$.
A matrix where one of the dimensions equals one is often called a ''vector'', and interpreted as an element of real coordinate space. An $m\; imes\; 1$ matrix (one column and $m$ rows) is called a column vector and a $1\; imes\; n$ matrix (one row and $n$ columns) is called a row vector.

Example

The matrix
:
9 & 8 & 6 \
1 & 2 & 7 \
4 & 9 & 2 \
6 & 0 & 5 end{bmatrix}   or  
9 & 8 & 6 \
1 & 2 & 7 \
4 & 9 & 2 \
6 & 0 & 5 end{pmatrix}
is a $4\; imes\; 3$ matrix. The element $a\_\{2,3\}$ or $A[2,3]$ is 7.
The matrix
:
is a $1\; imes\; 9$ matrix, or 9-element row vector.

Adding and multiplying matrices

Sum

Main articles: Matrix addition

Two or more matrices of identical dimensions $m$ and $n$ can be added. 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. $A+B=\; (a\_\{i,j\})\_\{1le\; i\; le\; m;\; 1le\; j\; le\; n\}\; +\; (b\_\{i,j\})\_\{1le\; i\; le\; m;\; 1le\; j\; le\; n\}\; =\; (a\_\{i,j\}+b\_\{i,j\})\_\{1le\; i\; le\; m;\; 1le\; j\; le\; n\}$ ). For example:
: $$
egin{bmatrix}
1 & 3 & 2 \
1 & 0 & 0 \
1 & 2 & 2
end{bmatrix}
+
egin{bmatrix}
0 & 0 & 5 \
7 & 5 & 0 \
2 & 1 & 1
end{bmatrix}
=
egin{bmatrix}
1+0 & 3+0 & 2+5 \
1+7 & 0+5 & 0+0 \
1+2 & 2+1 & 2+1
end{bmatrix}
=
egin{bmatrix}
1 & 3 & 7 \
8 & 5 & 0 \
3 & 3 & 3
end{bmatrix}

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

Scalar multiplication

Main articles: Matrix multiplication

Given a matrix $A$ and a number $c$, the 'scalar multiplication' $cA$ is computed by multiplying every element of $A$ by the scalar $c$ (i.e. $(cA)\_\{i,j\}\; =\; c\; cdot\; a\_\{i,j\}$ ). For example:
: $2\; cdot$
egin{bmatrix}
1 & 8 & -3 \
4 & -2 & 5
end{bmatrix}
=
egin{bmatrix}
2 cdot 1 & 2cdot 8 & 2cdot -3 \
2cdot 4 & 2cdot -2 & 2cdot 5
end{bmatrix}
=
egin{bmatrix}
2 & 16 & -6 \
8 & -4 & 10
end{bmatrix}

Matrix addition and scalar multiplication turn the set $ext\{M\}(m,n,mathbb\{R\})$ of all $m$-by-$n$ matrices with real entries into a real vector space of dimension $mcdot\; n$.

Matrix multiplication

Main articles: 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 given by:
:$$
(AB)_{i,j} = a_{i,1} b_{1,j} + a_{i,2} b_{2,j} + ldots + a_{i,n} b_{n,j}

for each pair $(i,j)$.
For example:
: $$
egin{bmatrix}
1 & 0 & 2 \
-1 & 3 & 1 \
end{bmatrix}
imes
egin{bmatrix}
3 & 1 \
2 & 1 \
1 & 0 \
end{bmatrix}
=
egin{bmatrix}
( 1 imes 3 + 0 imes 2 + 2 imes 1)
& ( 1 imes 1 + 0 imes 1 + 2 imes 0) \
(-1 imes 3 + 3 imes 2 + 1 imes 1)
& (-1 imes 1 + 3 imes 1 + 1 imes 0) \
end{bmatrix}

:::::::: $$
=
egin{bmatrix}
5 & 1 \
4 & 2 \
end{bmatrix}.

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 $AB$
e BA.

Linear transformations, ranks and transpose

Main articles: Transformation matrix

Main articles: 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_n=M'' and ''I_nN=N'' for any ''m''-by-''n'' matrix ''M'' and ''n''-by-''k'' matrix ''N''.
For example, if ''n'' = 3:
: $$
I_3 =
egin{bmatrix}
1 & 0 & 0 \
0 & 1 & 0 \
0 & 0 & 1
end{bmatrix}
.
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 and in Dutch.) 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, $a\_\{i,j\}=a\_\{j,i\}\; Leftrightarrow\; A^mathrm\{T\}\; =\; A$.

★ Skew-symmetric matrices are such that elements symmetric about the ''main diagonal'' are the negative of each other, that is, $a\_\{i,j\}=-a\_\{j,i\}\; Leftrightarrow\; A^mathrm\{T\}=-A$. In a skew-symmetric matrix, all diagonal elements are zero, that is, $a\_\{i,i\}=-a\_\{i,i\}Rightarrow\; a\_\{i,i\}=0$.

★ Hermitian (or self-adjoint) matrices are such that elements symmetric about the diagonal are each others complex conjugates, that is, $a\_\{i,j\}=overline\{a\}\_\{j,i\}\; Leftrightarrow\; A^mathrm\{H\}\; =\; A$, where $overline\{z\}$ signifies the complex conjugate of a complex number $z$ and $,!\; A^mathrm\{H\}$ the conjugate transpose of $A$.

★ Toeplitz matrices have common elements on their diagonals, that is, $,!\; a\_\{i,j\}=a\_\{i+1,j+1\}$.

★ Stochastic matrices are square matrices whose rows are probability vectors; they are used to define Markov chains.

★ A square matrix $A$ is called idempotent if $A^2=AA=A$.
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. A 3-by-3 magic square appears in Chinese literature dating from as early as 650 BC.Swaney, Mark. History of Magic Squares.
Matrices have a long history of application in solving linear equations. An important Chinese text from between 300 BC and AD 200, ''The Nine Chapters on the Mathematical Art'' (''Jiu Zhang Suan Shu''), is the first example of the use of matrix methods to solve simultaneous equations.^[1] In the seventh chapter, "Too much and not enough," the concept of a determinant first appears almost 2000 years before its publication by the Japanese mathematician Seki Kowa in 1683 and the German mathematician Gottfried Leibniz in 1693.
Magic squares were known to Arab mathematicians, possibly as early as the 7th century, when the Arabs conquered northwestern parts of the Indian subcontinent and learned Indian mathematics and astronomy, including other aspects of combinatorial mathematics. It has also been suggested that the idea came via China. The first magic squares of order 5 and 6 appear in an encyclopedia from Baghdad ''circa'' 983 AD, the ''Encyclopedia of the Brethren of Purity'' (''Rasa'il Ihkwan al-Safa''); simpler magic squares were known to several earlier Arab mathematicians.
After the development of the theory of determinants by Seki Kowa and Leibniz in the late 17th century, Cramer developed the theory further in the 18th century, presenting Cramer's rule in 1750. Carl Friedrich Gauss and Wilhelm Jordan developed Gauss-Jordan elimination in the 1800s.
The term "matrix" was 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) used matrix theory to investigate an aerodynamic phenomenon called fluttering or aeroelasticity during WWII.

Applications

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

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

Further reading

A more advanced article on matrices is ''Matrix theory''.

See also

★ List of matrices

★ Logical matrix

★ Relation composition

★ Matrix calculus

References

1. Nine Chapters of the Mathematical Art, Companion and Commentary, Shen Kangshen et al. (ed.), , , Oxford University Press, 1999, cited by Linear Algebra with Applications, Otto Bretscher, , , Prentice-Hall, 2005,

External links

★ Resources

★
★ Matrix name and history: very brief overview, ualr.edu

★
★ Introduction to Matrix Algebra: definitions and properties, xycoon.com

★
★ Matrix Algebra, sosmath.com

★
★ The Matrix Reference Manual, Imperial College

★
★ An online textbook on Introduction to Matrix Algebra at ''Holistic Numerical Methods Institute''

★
★ Applied examples of matrices used in graphical game programming, Riemer's DirectX Tutorials

★ Online Matrix Calculators

★
★ easycalculation.com

★
★ bluebit.gr

★
★ wims.unice.fr

★ Freeware

★
★ MATRIX 2.1 Excel add-in, foxes

★
★ MacAnova, University of Minnesota School of Statistics