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In
linear algebra Linear algebra is the branch of mathematics concerning linear equations such as: :a_1x_1+\cdots +a_nx_n=b, linear maps such as: :(x_1, \ldots, x_n) \mapsto a_1x_1+\cdots +a_nx_n, and their representations in vector spaces and through matrices. ...
, a coefficient matrix is a
matrix Matrix most commonly refers to: * ''The Matrix'' (franchise), an American media franchise ** ''The Matrix'', a 1999 science-fiction action film ** "The Matrix", a fictional setting, a virtual reality environment, within ''The Matrix'' (franchis ...
consisting of the
coefficient In mathematics, a coefficient is a multiplicative factor in some term of a polynomial, a series, or an expression; it is usually a number, but may be any expression (including variables such as , and ). When the coefficients are themselves var ...
s of the variables in a set of
linear equations In mathematics, a linear equation is an equation that may be put in the form a_1x_1+\ldots+a_nx_n+b=0, where x_1,\ldots,x_n are the variables (or unknowns), and b,a_1,\ldots,a_n are the coefficients, which are often real numbers. The coefficien ...
. The matrix is used in solving
systems of linear equations In mathematics, a system of linear equations (or linear system) is a collection of one or more linear equations involving the same variables. For example, :\begin 3x+2y-z=1\\ 2x-2y+4z=-2\\ -x+\fracy-z=0 \end is a system of three equations in th ...
.


Coefficient matrix

In general, a system with ''m''
linear equations In mathematics, a linear equation is an equation that may be put in the form a_1x_1+\ldots+a_nx_n+b=0, where x_1,\ldots,x_n are the variables (or unknowns), and b,a_1,\ldots,a_n are the coefficients, which are often real numbers. The coefficien ...
and ''n'' unknowns can be written as : \begin a_ x_1 + a_ x_2 + \cdots + a_ x_n &= b_1 \\ a_ x_1 + a_ x_2 + \cdots + a_ x_n &= b_2 \\ &\;\; \vdots \\ a_ x_1 + a_ x_2 + \cdots + a_ x_n &= b_m \end where x_1, x_2, \ldots, x_n are the unknowns and the numbers a_, a_, \ldots, a_ are the coefficients of the system. The coefficient matrix is the ''m'' × ''n'' matrix with the coefficient a_ as the (''i'', ''j'')th entry: : \begin a_ & a_ & \cdots & a_ \\ a_ & a_ &\cdots & a_ \\ \vdots & \vdots & \ddots & \vdots \\ a_ & a_ & \cdots & a_ \end Then the above set of equations can be expressed more succinctly as : A\mathbf = \mathbf where ''A'' is the coefficient matrix and b is the column vector of constant terms.


Relation of its properties to properties of the equation system

By the
Rouché–Capelli theorem In linear algebra, the Rouché–Capelli theorem determines the number of solutions for a system of linear equations, given the rank of its augmented matrix and coefficient matrix. The theorem is variously known as the: * Rouché–Capelli theore ...
, the system of equations is
inconsistent In classical deductive logic, a consistent theory is one that does not lead to a logical contradiction. The lack of contradiction can be defined in either semantic or syntactic terms. The semantic definition states that a theory is consistent ...
, meaning it has no solutions, if the
rank Rank is the relative position, value, worth, complexity, power, importance, authority, level, etc. of a person or object within a ranking, such as: Level or position in a hierarchical organization * Academic rank * Diplomatic rank * Hierarchy * H ...
of the
augmented matrix In linear algebra, an augmented matrix is a matrix obtained by appending the columns of two given matrices, usually for the purpose of performing the same elementary row operations on each of the given matrices. Given the matrices and , where ...
(the coefficient matrix augmented with an additional column consisting of the vector b) is greater than the rank of the coefficient matrix. If, on the other hand, the ranks of these two matrices are equal, the system must have at least one solution. The solution is unique if and only if the rank ''r'' equals the number ''n'' of variables. Otherwise the general solution has ''n'' – ''r ''free parameters; hence in such a case there are an infinitude of solutions, which can be found by imposing arbitrary values on ''n'' – ''r'' of the variables and solving the resulting system for its unique solution; different choices of which variables to fix, and different fixed values of them, give different system solutions.


Dynamic equations

A first-order
matrix difference equation A matrix difference equation is a difference equation in which the value of a vector (or sometimes, a matrix) of variables at one point in time is related to its own value at one or more previous points in time, using matrices. The order of the eq ...
with constant term can be written as :\mathbf_ = A \mathbf_t + \mathbf, where ''A'' is ''n'' × ''n'' and y and c are ''n'' × 1. This system converges to its steady-state level of ''y''
if and only if In logic and related fields such as mathematics and philosophy, "if and only if" (shortened as "iff") is a biconditional logical connective between statements, where either both statements are true or both are false. The connective is bicondi ...
the
absolute value In mathematics, the absolute value or modulus of a real number x, is the non-negative value without regard to its sign. Namely, , x, =x if is a positive number, and , x, =-x if x is negative (in which case negating x makes -x positive), an ...
s of all ''n''
eigenvalue In linear algebra, an eigenvector () or characteristic vector of a linear transformation is a nonzero vector that changes at most by a scalar factor when that linear transformation is applied to it. The corresponding eigenvalue, often denoted b ...
s of ''A'' are less than 1. A first-order
matrix differential equation A differential equation is a mathematical equation for an unknown function of one or several variables that relates the values of the function itself and its derivatives of various orders. A matrix differential equation contains more than one funct ...
with constant term can be written as :\frac = A\mathbf(t) + \mathbf. This system is stable if and only if all ''n'' eigenvalues of ''A'' have negative real parts.


References

{{DEFAULTSORT:Coefficient Matrix Linear algebra