Learning Center
Plans & pricing Sign in
Sign Out




In mathematics, a matrix (plural matrices) is a rectangular array of numbers, symbols, or expressions,
arranged in rows and columns. The individual items in a matrix are called its elements or entries. An
example of a matrix with 2 rows and 3 columns is

Matrices of the same size can be added or subtracted element by element. The rule for matrix
multiplication is more complicated, and two matrices can be multiplied only when the number of
columns in the first equals the number of rows in the second. A major application of matrices is to
represent linear transformations, that is, generalizations of linear functions such as f(x) = 4x. For
example, the rotation of vectors in three dimensional space is a linear transformation.

If R is a rotation matrix and v is a column vector (a matrix with only one column) describing the
position of a point in space, the product Rv is a column vector describing the position of that point after
a rotation. The product of two matrices is a matrix that represents the composition of two linear
transformations. Another application of matrices is in the solution of a system of linear equations. If the
matrix is square, it is possible to deduce some of its properties by computing its determinant. For
example, a square matrix has an inverse if and only if its determinant is not zero. Eigenvalues and
eigenvectors provide insight into the geometry of linear transformations.

                                                                     Know More About :- Triangles                                                             Page : 1/3
Matrices find applications in most scientific fields. In physics, matrices are used to study electrical
circuits, optics, and quantum mechanics. In computer graphics, matrices are used to project a 3-
dimensional image onto a 2-dimensional screen, and to create realistic-seeming motion. Matrix calculus
generalizes classical analytical notions such as derivatives and exponentials to higher dimensions.

A major branch of numerical analysis is devoted to the development of efficient algorithms for matrix
computations, a subject that is centuries old and is today an expanding area of research. Matrix
decomposition methods simplify computations, both theoretically and practically. Algorithms that are
tailored to the structure of particular matrix structures, e.g. sparse matrices and near-diagonal matrices,
expedite computations in finite element method and other computations. Infinite matrices occur in
planetary theory and in atomic theory. A simple example is the matrix representing the derivative
operator, which acts on the Taylor series of a function.

A matrix is a rectangular arrangement of mathematical expressions that can be simply numbers.[1]
Commonly the m components of the matrix are written in a rectangular arrangement in the form of a
column of m rows: An alternative notation uses large parentheses instead of box brackets. The
horizontal and vertical lines in a matrix are called rows and columns, respectively. The numbers in the
matrix are called its entries or its elements. To specify the size of a matrix, a matrix with m rows and n
columns is called an m-by-n matrix or m × n matrix, while m and n are called its dimensions. The
above is a 4-by-3 matrix.

A matrix with one row (a 1 × n matrix) is called a row vector, and a matrix with one column (an m × 1
matrix) is called a column vector. Any row or column of a matrix determines a row or column vector,
obtained by removing all other rows or columns respectively from the matrix. For example, the row
vector for the third row of the above matrix A is

When a row or column of a matrix is interpreted as a value, this refers to the corresponding row or
column vector. For instance one may say that two different rows of a matrix are equal, meaning they
determine the same row vector. In some cases the value of a row or column should be interpreted just as
a sequence of values (an element of Rn if entries are real numbers) rather than as a matrix, for instance
when saying that the rows of a matrix are equal to the corresponding columns of its transpose matrix.

                                                              Read More About :- Whole Numbers                                                             Page : 2/3
     Thank You


To top