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Continue shopping Checkout Continue shopping. A matrix with the same number of rows and columns is called a square matrix. A matrix with an infinite number of rows or columns or both is called an infinite matrix. In some contexts, such as computer algebra programs, it is useful to consider a matrix with no rows or no columns, called an empty matrix.
Matrices are commonly written in box brackets or parentheses :. The specifics of symbolic matrix notation vary widely, with some prevailing trends. Matrices are usually symbolized using upper-case letters such as A in the examples above , while the corresponding lower-case letters, with two subscript indices for example, a 11 , or a 1,1 , represent the entries. In addition to using upper-case letters to symbolize matrices, many authors use a special typographical style , commonly boldface upright non-italic , to further distinguish matrices from other mathematical objects.
The entry in the i -th row and j -th column of a matrix A is sometimes referred to as the i , j , i , j , or i , j th entry of the matrix, and most commonly denoted as a i , j , or a ij. Alternative notations for that entry are A [ i,j ] or A i,j. For example, the 1,3 entry of the following matrix A is 5 also denoted a 13 , a 1,3 , A [ 1,3 ] or A 1,3 :.
In this case, the matrix itself is sometimes defined by that formula, within square brackets or double parentheses. An asterisk is occasionally used to refer to whole rows or columns in a matrix. There are a number of basic operations that can be applied to modify matrices, called matrix addition , scalar multiplication , transposition , matrix multiplication , row operations , and submatrix.
This operation is called scalar multiplication , but its result is not named "scalar product" to avoid confusion, since "scalar product" is sometimes used as a synonym for " inner product ". Multiplication of two matrices is defined if and 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 whose entries are given by dot product of the corresponding row of A and the corresponding column of B :.
Even if both products are defined, they need not be equal, that is, generally.
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An example of two matrices not commuting with each other is:. Besides the ordinary matrix multiplication just described, there exist other less frequently used operations on matrices that can be considered forms of multiplication, such as the Hadamard product and the Kronecker product. These operations are used in a number of ways, including solving linear equations and finding matrix inverses. The minors and cofactors of a matrix are found by computing the determinant of certain submatrices.
A principal submatrix is a square submatrix obtained by removing certain rows and columns. The definition varies from author to author. According to some authors, a principal submatrix is a submatrix in which the set of row indices that remain is the same as the set of column indices that remain. Matrices can be used to compactly write and work with multiple linear equations, that is, systems of linear equations.
Using matrices, this can be solved more compactly than would be possible by writing out all the equations separately. If A has no inverse, solutions if any can be found using its generalized inverse. Matrices and matrix multiplication reveal their essential features when related to linear transformations , also known as linear maps. The matrix A is said to represent the linear map f , and A is called the transformation matrix of f. These vectors define the vertices of the unit square. The following table shows a number of 2-by-2 matrices with the associated linear maps of R 2.
The blue original is mapped to the green grid and shapes. The origin 0,0 is marked with a black point. The rank of a matrix A is the maximum number of linearly independent row vectors of the matrix, which is the same as the maximum number of linearly independent column vectors. A square matrix is a matrix with the same number of rows and columns. An n -by- n matrix is known as a square matrix of order n. Any two square matrices of the same order can be added and multiplied. The entries a ii form the main diagonal of a square matrix.
They lie on the imaginary line that runs from the top left corner to the bottom right corner of the matrix. If all entries of A below the main diagonal are zero, A is called an upper triangular matrix. Similarly if all entries of A above the main diagonal are zero, A is called a lower triangular matrix. If all entries outside the main diagonal are zero, A is called a diagonal matrix. The identity matrix I n of size n is the n -by- n matrix in which all the elements on the main diagonal are equal to 1 and all other elements are equal to 0, for example,.
It is a square matrix of order n , and also a special kind of diagonal matrix. It is called an identity matrix because multiplication with it leaves a matrix unchanged:. A nonzero scalar multiple of an identity matrix is called a scalar matrix. If the matrix entries come from a field, the scalar matrices form a group, under matrix multiplication, that is isomorphic to the multiplicative group of nonzero elements of the field.
By the spectral theorem , real symmetric matrices and complex Hermitian matrices have an eigenbasis ; that is, every vector is expressible as a linear combination of eigenvectors. In both cases, all eigenvalues are real.
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A square matrix A is called invertible or non-singular if there exists a matrix B such that. A symmetric matrix is positive-definite if and only if all its eigenvalues are positive, that is, the matrix is positive-semidefinite and it is invertible. Allowing as input two different vectors instead yields the bilinear form associated to A :. An orthogonal matrix is a square matrix with real entries whose columns and rows are orthogonal unit vectors that is, orthonormal vectors. Equivalently, a matrix A is orthogonal if its transpose is equal to its inverse :. The identity matrices have determinant 1 , and are pure rotations by an angle zero.
The complex analogue of an orthogonal matrix is a unitary matrix. The trace , tr A of a square matrix A is the sum of its diagonal entries. While matrix multiplication is not commutative as mentioned above , the trace of the product of two matrices is independent of the order of the factors:. Also, the trace of a matrix is equal to that of its transpose, that is,.
The determinant det A or A of a square matrix A is a number encoding certain properties of the matrix. A matrix is invertible if and only if its determinant is nonzero. Its absolute value equals the area in R 2 or volume in R 3 of the image of the unit square or cube , while its sign corresponds to the orientation of the corresponding linear map: the determinant is positive if and only if the orientation is preserved. The determinant of 3-by-3 matrices involves 6 terms rule of Sarrus. The more lengthy Leibniz formula generalises these two formulae to all dimensions. Adding a multiple of any row to another row, or a multiple of any column to another column, does not change the determinant.
Finally, the Laplace expansion expresses the determinant in terms of minors , that is, determinants of smaller matrices. Determinants can be used to solve linear systems using Cramer's rule , where the division of the determinants of two related square matrices equates to the value of each of the system's variables. It is a monic polynomial of degree n. Matrix calculations can be often performed with different techniques. Many problems can be solved by both direct algorithms or iterative approaches.
For example, the eigenvectors of a square matrix can be obtained by finding a sequence of vectors x n converging to an eigenvector when n tends to infinity. To choose the most appropriate algorithm for each specific problem, it is important to determine both the effectiveness and precision of all the available algorithms. The domain studying these matters is called numerical linear algebra.
Determining the complexity of an algorithm means finding upper bounds or estimates of how many elementary operations such as additions and multiplications of scalars are necessary to perform some algorithm, for example, multiplication of matrices. For example, calculating the matrix product of two n -by- n matrix using the definition given above needs n 3 multiplications, since for any of the n 2 entries of the product, n multiplications are necessary. The Strassen algorithm outperforms this "naive" algorithm; it needs only n 2. In many practical situations additional information about the matrices involved is known.
An important case are sparse matrices , that is, matrices most of whose entries are zero. An algorithm is, roughly speaking, numerically stable, if little deviations in the input values do not lead to big deviations in the result. For example, calculating the inverse of a matrix via Laplace expansion adj A denotes the adjugate matrix of A. The norm of a matrix can be used to capture the conditioning of linear algebraic problems, such as computing a matrix's inverse.
Although most computer languages are not designed with commands or libraries for matrices, as early as the s, some engineering desktop computers such as the HP had ROM cartridges to add BASIC commands for matrices. Some computer languages such as APL were designed to manipulate matrices, and various mathematical programs can be used to aid computing with matrices.
There are several methods to render matrices into a more easily accessible form. They are generally referred to as matrix decomposition or matrix factorization techniques.
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The interest of all these techniques is that they preserve certain properties of the matrices in question, such as determinant, rank or inverse, so that these quantities can be calculated after applying the transformation, or that certain matrix operations are algorithmically easier to carry out for some types of matrices. The LU decomposition factors matrices as a product of lower L and an upper triangular matrices U.
Likewise, inverses of triangular matrices are algorithmically easier to calculate. The Gaussian elimination is a similar algorithm; it transforms any matrix to row echelon form. This can be used to compute the matrix exponential e A , a need frequently arising in solving linear differential equations , matrix logarithms and square roots of matrices. Matrices can be generalized in different ways. Abstract algebra uses matrices with entries in more general fields or even rings , while linear algebra codifies properties of matrices in the notion of linear maps. It is possible to consider matrices with infinitely many columns and rows.
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Another extension are tensors , which can be seen as higher-dimensional arrays of numbers, as opposed to vectors, which can often be realised as sequences of numbers, while matrices are rectangular or two-dimensional arrays of numbers. Similarly under certain conditions matrices form rings known as matrix rings.
Though the product of matrices is not in general commutative yet certain matrices form fields known as matrix fields. This article focuses on matrices whose entries are real or complex numbers. However, matrices can be considered with much more general types of entries than real or complex numbers. As a first step of generalization, any field , that is, a set where addition , subtraction , multiplication and division operations are defined and well-behaved, may be used instead of R or C , for example rational numbers or finite fields.
For example, coding theory makes use of matrices over finite fields. Wherever eigenvalues are considered, as these are roots of a polynomial they may exist only in a larger field than that of the entries of the matrix; for instance they may be complex in case of a matrix with real entries. The possibility to reinterpret the entries of a matrix as elements of a larger field for example, to view a real matrix as a complex matrix whose entries happen to be all real then allows considering each square matrix to possess a full set of eigenvalues.
Alternatively one can consider only matrices with entries in an algebraically closed field , such as C , from the outset.
More generally, matrices with entries in a ring R are widely used in mathematics. The very same addition and multiplication operations of matrices extend to this setting, too. The set M n , R of all square n -by- n matrices over R is a ring called matrix ring , isomorphic to the endomorphism ring of the left R - module R n. The determinant of square matrices over a commutative ring R can still be defined using the Leibniz formula ; such a matrix is invertible if and only if its determinant is invertible in R , generalising the situation over a field F , where every nonzero element is invertible.
One special but common case is block matrices , which may be considered as matrices whose entries themselves are matrices. The entries need not be square matrices, and thus need not be members of any ring ; but their sizes must fulfil certain compatibility conditions. In other words, column j of A expresses the image of v j in terms of the basis vectors w i of W ; thus this relation uniquely determines the entries of the matrix A. The matrix depends on the choice of the bases: different choices of bases give rise to different, but equivalent matrices.
A group is a mathematical structure consisting of a set of objects together with a binary operation , that is, an operation combining any two objects to a third, subject to certain requirements. Any property of matrices that is preserved under matrix products and inverses can be used to define further matrix groups.
For example, matrices with a given size and with a determinant of 1 form a subgroup of that is, a smaller group contained in their general linear group, called a special linear group. Orthogonal matrices with determinant 1 form a subgroup called special orthogonal group. Every finite group is isomorphic to a matrix group, as one can see by considering the regular representation of the symmetric group.
All that matters is that for every element in the set indexing rows, and every element in the set indexing columns, there is a well-defined entry these index sets need not even be subsets of the natural numbers. The basic operations of addition, subtraction, scalar multiplication, and transposition can still be defined without problem; however matrix multiplication may involve infinite summations to define the resulting entries, and these are not defined in general.
If infinite matrices are used to describe linear maps, then only those matrices can be used all of whose columns have but a finite number of nonzero entries, for the following reason. Now the columns of A describe the images by f of individual basis vectors of V in the basis of W , which is only meaningful if these columns have only finitely many nonzero entries. Moreover, this amounts to forming a linear combination of the columns of A that effectively involves only finitely many of them, whence the result has only finitely many nonzero entries, because each of those columns does.
Products of two matrices of the given type is well defined provided that the column-index and row-index sets match , is of the same type, and corresponds to the composition of linear maps. If R is a normed ring , then the condition of row or column finiteness can be relaxed. With the norm in place, absolutely convergent series can be used instead of finite sums.
For example, the matrices whose column sums are absolutely convergent sequences form a ring. Analogously, the matrices whose row sums are absolutely convergent series also form a ring. Infinite matrices can also be used to describe operators on Hilbert spaces , where convergence and continuity questions arise, which again results in certain constraints that must be imposed. However, the explicit point of view of matrices tends to obfuscate the matter,  and the abstract and more powerful tools of functional analysis can be used instead. An empty matrix is a matrix in which the number of rows or columns or both is zero.
For example, if A is a 3-by-0 matrix and B is a 0-by-3 matrix, then AB is the 3-by-3 zero matrix corresponding to the null map from a 3-dimensional space V to itself, while BA is a 0-by-0 matrix. There is no common notation for empty matrices, but most computer algebra systems allow creating and computing with them. The determinant of the 0-by-0 matrix is 1 as follows from regarding the empty product occurring in the Leibniz formula for the determinant as 1. There are numerous applications of matrices, both in mathematics and other sciences. Some of them merely take advantage of the compact representation of a set of numbers in a matrix.
For example, in game theory and economics , the payoff matrix encodes the payoff for two players, depending on which out of a given finite set of alternatives the players choose. For example, 2-by-2 rotation matrices represent the multiplication with some complex number of absolute value 1, as above. A similar interpretation is possible for quaternions  and Clifford algebras in general. Early encryption techniques such as the Hill cipher also used matrices. However, due to the linear nature of matrices, these codes are comparatively easy to break.
Chemistry makes use of matrices in various ways, particularly since the use of quantum theory to discuss molecular bonding and spectroscopy. Examples are the overlap matrix and the Fock matrix used in solving the Roothaan equations to obtain the molecular orbitals of the Hartree—Fock method.