matrix

/may"triks, ma"-/, n., pl. matrices /may"tri seez', ma"-/, matrixes.

1. something that constitutes the place or point from which something else originates, takes form, or develops: The Greco-Roman world was the matrix for Western civilization.

2. Anat. a formative part, as the corium beneath a nail.

3. Biol.

a. the intercellular substance of a tissue.

b. See ground substance.

4. Petrol. the fine-grained portion of a rock in which coarser crystals or rock fragments are embedded.

5. fine material, as cement, in which lumps of coarser material, as of an aggregate, are embedded.

6. Mining. gangue.

7. Metall. a crystalline phase in an alloy in which other phases are embedded.

8. Print. a mold for casting typefaces.

9. master (def. 18).

10. (in a press or stamping machine) a multiple die or perforated block on which the material to be formed is placed.

11. Math. a rectangular array of numbers, algebraic symbols, or mathematical functions, esp. when such arrays are added and multiplied according to certain rules.

12. Ling. a rectangular display of features characterizing a set of linguistic items, esp. phonemes, usually presented as a set of columns of plus or minus signs specifying the presence or absence of each feature for each item.

13. Also called master. a mold made by electroforming from a disk recording, from which other disks may be pressed.

14. Archaic. the womb.

[1325-75; ME matris, matrix < L matrix female animal kept for breeding (LL: register, orig. of such beasts), parent stem (of plants), deriv. of mater mother]

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Set of numbers arranged in rows and columns to form a rectangular array.

Matrix elements may also be differential operators, vectors, or functions. Matrices have wide applications in engineering, physics, economics, and statistics, as well as in various branches of mathematics. They are usually first encountered in the study of systems of equations represented by matrix equations of the form Ax = B, which may be solved by finding the inverse of matrix A or by using an algebraic method based on its determinant.

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▪ geology

      in geology, the material in which something is embedded, either the natural rock that holds crystals, fossils, pebbles, mineral veins, and the like, or the fine-grained materials that surround larger grains in a rock—e.g., silt and clay particles in a sandstone or tiny crystals in a crystalline rock, sometimes called groundmass.

▪ mathematics

      a set of numbers arranged in rows and columns so as to form a rectangular array. The numbers are called the elements, or entries, of the matrix. Matrices have wide applications in engineering, physics, economics, and statistics as well as in various branches of mathematics. Historically, it was not the matrix but a certain number associated with a square array of numbers called the determinant that was first recognized. Only gradually did the idea of the matrix as an algebraic entity emerge. The term matrix was introduced by the 19th-century English mathematician James Sylvester (Sylvester, James Joseph), but it was his friend the mathematician Arthur Cayley (Cayley, Arthur) who developed the algebraic aspect of matrices in two papers in the 1850s. Cayley first applied them to the study of systems of linear equations, where they are still very useful. They are also important because, as Cayley recognized, certain sets of matrices form algebraic systems in which many of the ordinary laws of arithmetic (e.g., the associative and distributive laws) are valid but in which other laws (e.g., the commutative law) are not valid. Matrices have also come to have important applications in computer graphics, where they have been used to represent rotations and other transformations of images.

      If there are m rows and n columns, the matrix is said to be an “m by n” matrix, written “m × n.” For example,

      is a 2 × 3 matrix. A matrix with n rows and n columns is called a square matrix of order n. An ordinary number can be regarded as a 1 × 1 matrix; thus, 3 can be thought of as the matrix [3].

      In a common notation, a capital letter denotes a matrix, and the corresponding small letter with a double subscript describes an element of the matrix. Thus, a_ij is the element in the ith row and jth column of the matrix A. If A is the 2 × 3 matrix shown above, then a₁₁ = 1, a₁₂ = 3, a₁₃ = 8, a₂₁ = 2, a₂₂ = −4, and a₂₃ = 5. Under certain conditions, matrices can be added and multiplied as individual entities, giving rise to important mathematical systems known as matrix algebras.

      Matrices occur naturally in systems of simultaneous equations. In the following system for the unknowns x and y,

      the array of numbers

      is a matrix whose elements are the coefficients of the unknowns. The solution of the equations depends entirely on these numbers and on their particular arrangement. If 3 and 4 were interchanged, the solution would not be the same.

      Two matrices A and B are equal to one another if they possess the same number of rows and the same number of columns and if a_ij = b_ij for each i and each j. If A and B are two m × n matrices, their sum S = A + B is the m × n matrix whose elements s_ij = a_ij + b_ij. That is, each element of S is equal to the sum of the elements in the corresponding positions of A and B.

      A matrix A can be multiplied by an ordinary number c, which is called a scalar. The product is denoted by cA or Ac and is the matrix whose elements are ca_ij.

      The multiplication of a matrix A by a matrix B to yield a matrix C is defined only when the number of columns of the first matrix A equals the number of rows of the second matrix B. To determine the element c_ij, which is in the ith row and jth column of the product, the first element in the ith row of A is multiplied by the first element in the jth column of B, the second element in the row by the second element in the column, and so on until the last element in the row is multiplied by the last element of the column; the sum of all these products gives the element c_ij. In symbols, for the case where A has m columns and B has m rows,

The matrix C has as many rows as A and as many columns as B.

      Unlike the multiplication of ordinary numbers a and b, in which ab always equals ba, the multiplication of matrices A and B is not commutative. It is, however, associative and distributive over addition. That is, when the operations are possible, the following equations always hold true: A(BC) = (AB)C, A(B + C) = AB + AC, and (B + C)A = BA + CA. If the 2 × 2 matrix A whose rows are (2, 3) and (4, 5) is multiplied by itself, then the product, usually written A², has rows (16, 21) and (28, 37).

      A matrix O with all its elements 0 is called a zero matrix. A square matrix A with 1s on the main diagonal (upper left to lower right) and 0s everywhere else is called a unit matrix. It is denoted by I or I_n to show that its order is n. If B is any square matrix and I and O are the unit and zero matrices of the same order, it is always true that B + O = O + B = B and BI = IB = B. Hence O and I behave like the 0 and 1 of ordinary arithmetic. In fact, ordinary arithmetic is the special case of matrix arithmetic in which all matrices are 1 × 1.

      Associated with each square matrix A is a number that is known as the determinant of A, denoted det A. For example, for the 2 × 2 matrix

det A = ad − bc. A square matrix B is called nonsingular if det B ≠ 0. If B is nonsingular, there is a matrix called the inverse of B, denoted B⁻¹, such that BB⁻¹ = B⁻¹B = I. The equation AX = B, in which A and B are known matrices and X is an unknown matrix, can be solved uniquely if A is a nonsingular matrix, for then A⁻¹ exists and both sides of the equation can be multiplied on the left by it: A⁻¹(AX) = A⁻¹B. Now A⁻¹(AX) = (A⁻¹A)X = IX = X; hence the solution is X = A⁻¹B. A system of m linear equations in n unknowns can always be expressed as a matrix equation AX = B in which A is the m × n matrix of the coefficients of the unknowns, X is the n × 1 matrix of the unknowns, and B is the n × 1 matrix containing the numbers on the right-hand side of the equation.

      A problem of great significance in many branches of science is the following: given a square matrix A of order n, find the n × 1 matrix X, called an n-dimensional vector, such that AX = cX. Here c is a number called an eigenvalue, and X is called an eigenvector. The existence of an eigenvector X with eigenvalue c means that a certain transformation of space associated with the matrix A stretches space in the direction of the vector X by the factor c.

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