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 matrix (mathemat ...
, Cramer's rule is an explicit formula for the solution of a
system of linear equations
In mathematics, a system of linear equations (or linear system) is a collection of two or more linear equations involving the same variable (math), variables.
For example,
: \begin
3x+2y-z=1\\
2x-2y+4z=-2\\
-x+\fracy-z=0
\end
is a system of th ...
with as many equations as unknowns, valid whenever the system has a unique solution. It expresses the solution in terms of the
determinant
In mathematics, the determinant is a Scalar (mathematics), scalar-valued function (mathematics), function of the entries of a square matrix. The determinant of a matrix is commonly denoted , , or . Its value characterizes some properties of the ...
s of the (square)
coefficient matrix
In linear algebra, a coefficient matrix is a matrix consisting of the coefficients of the variables in a set of linear equations. The matrix is used in solving systems of linear equations.
Coefficient matrix
In general, a system with linear ...
and of
matrices obtained from it by replacing one column by the column vector of right-sides of the equations. It is named after
Gabriel Cramer, who published the rule for an arbitrary number of unknowns in 1750, although
Colin Maclaurin also published special cases of the rule in 1748, and possibly knew of it as early as 1729.
Cramer's rule, implemented in a naive way, is computationally inefficient for systems of more than two or three equations.
In the case of equations in unknowns, it requires computation of determinants, while
Gaussian elimination
In mathematics, Gaussian elimination, also known as row reduction, is an algorithm for solving systems of linear equations. It consists of a sequence of row-wise operations performed on the corresponding matrix of coefficients. This method can a ...
produces the result with the same (up to a constant factor independent of )
computational complexity as the computation of a single determinant.
Moreover,
Bareiss algorithm is a simple modification of Gaussian elimination that produces in a single computation a matrix whose nonzero entries are the determinants involved in Cramer's rule.
General case
Consider a system of linear equations for unknowns, represented in matrix multiplication form as follows:
:
where the matrix has a nonzero determinant, and the vector
is the column vector of the variables. Then the theorem states that in this case the system has a unique solution, whose individual values for the unknowns are given by:
:
where
is the matrix formed by replacing the -th column of by the column vector .
A more general version of Cramer's rule considers the matrix equation
:
where the matrix has a nonzero determinant, and , are matrices. Given sequences
and
, let
be the submatrix of with rows in
and columns in
. Let
be the matrix formed by replacing the
column of by the
column of , for all
. Then
:
In the case
, this reduces to the normal Cramer's rule.
The rule holds for systems of equations with coefficients and unknowns in any
field, not just in the
real number
In mathematics, a real number is a number that can be used to measure a continuous one- dimensional quantity such as a duration or temperature. Here, ''continuous'' means that pairs of values can have arbitrarily small differences. Every re ...
s.
Proof
The proof for Cramer's rule uses the following
properties of the determinants: linearity with respect to any given column and the fact that the determinant is zero whenever two columns are equal, which is implied by the property that the sign of the determinant flips if you switch two columns.
Fix the index of a column, and consider that the entries of the other columns have fixed values. This makes the determinant a function of the entries of the th column. Linearity with respect to this column means that this function has the form
:
where the
are coefficients that depend on the entries of that are not in column . So, one has
:
(
Laplace expansion provides a formula for computing the
but their expression is not important here.)
If the function
is applied to any ''other'' column of , then the result is the determinant of the matrix obtained from by replacing column by a copy of column , so the resulting determinant is 0 (the case of two equal columns).
Now consider a system of linear equations in unknowns
, whose coefficient matrix is , with det(''A'') assumed to be nonzero:
:
If one combines these equations by taking times the first equation, plus times the second, and so forth until times the last, then for every the resulting coefficient of becomes
:
So, all coefficients become zero, except the coefficient of
that becomes
Similarly, the constant coefficient becomes
and the resulting equation is thus
:
which gives the value of
as
:
As, by construction, the numerator is the determinant of the matrix obtained from by replacing column by , we get the expression of Cramer's rule as a necessary condition for a solution.
It remains to prove that these values for the unknowns form a solution. Let be the matrix that has the coefficients of
as th row, for
(this is the
adjugate matrix for ). Expressed in matrix terms, we have thus to prove that
:
is a solution; that is, that
:
For that, it suffices to prove that
:
where
is the
identity matrix
In linear algebra, the identity matrix of size n is the n\times n square matrix with ones on the main diagonal and zeros elsewhere. It has unique properties, for example when the identity matrix represents a geometric transformation, the obje ...
.
The above properties of the functions
show that one has , and therefore,
:
This completes the proof, since a
left inverse of a square matrix is also a right-inverse (see
Invertible matrix theorem).
For other proofs, see
below.
Finding inverse matrix
Let be an matrix with entries in a
field . Then
:
where denotes the
adjugate matrix, is the determinant, and is the
identity matrix
In linear algebra, the identity matrix of size n is the n\times n square matrix with ones on the main diagonal and zeros elsewhere. It has unique properties, for example when the identity matrix represents a geometric transformation, the obje ...
. If is nonzero, then the inverse matrix of is
:
This gives a formula for the inverse of , provided . In fact, this formula works whenever is a
commutative ring
In mathematics, a commutative ring is a Ring (mathematics), ring in which the multiplication operation is commutative. The study of commutative rings is called commutative algebra. Complementarily, noncommutative algebra is the study of ring prope ...
, provided that is a
unit. If is not a unit, then is not invertible over the ring (it may be invertible over a larger ring in which some non-unit elements of may be invertible).
Applications
Explicit formulas for small systems
Consider the linear system
:
which in matrix format is
:
Assume is nonzero. Then, with the help of
determinant
In mathematics, the determinant is a Scalar (mathematics), scalar-valued function (mathematics), function of the entries of a square matrix. The determinant of a matrix is commonly denoted , , or . Its value characterizes some properties of the ...
s, and can be found with Cramer's rule as
:
The rules for matrices are similar. Given
:
which in matrix format is
:
Then the values of and can be found as follows:
:
Differential geometry
Ricci calculus
Cramer's rule is used in the
Ricci calculus in various calculations involving the
Christoffel symbols of the first and second kind.
In particular, Cramer's rule can be used to prove that the divergence operator on a
Riemannian manifold
In differential geometry, a Riemannian manifold is a geometric space on which many geometric notions such as distance, angles, length, volume, and curvature are defined. Euclidean space, the N-sphere, n-sphere, hyperbolic space, and smooth surf ...
is invariant with respect to change of coordinates. We give a direct proof, suppressing the role of the Christoffel symbols.
Let
be a Riemannian manifold equipped with
local coordinates . Let
be a
vector field. We use the
summation convention throughout.
:Theorem.
:''The ''divergence'' of
,''
::
:''is invariant under change of coordinates.''
Let
be a
coordinate transformation with
non-singular Jacobian. Then the classical
transformation laws imply that
where
. Similarly, if
, then
.
Writing this transformation law in terms of matrices yields
, which implies
.
Now one computes
:
In order to show that this equals
,
it is necessary and sufficient to show that
:
which is equivalent to
:
Carrying out the differentiation on the left-hand side, we get:
:
where
denotes the matrix obtained from
by deleting the
th row and
th column.
But Cramer's Rule says that
:
is the
th entry of the matrix
.
Thus
:
completing the proof.
Computing derivatives implicitly
Consider the two equations
and
. When ''u'' and ''v'' are independent variables, we can define
and
An equation for
can be found by applying Cramer's rule.
First, calculate the first derivatives of ''F'', ''G'', ''x'', and ''y'':
:
Substituting ''dx'', ''dy'' into ''dF'' and ''dG'', we have:
:
Since ''u'', ''v'' are both independent, the coefficients of ''du'', ''dv'' must be zero. So we can write out equations for the coefficients:
:
Now, by Cramer's rule, we see that:
:
This is now a formula in terms of two
Jacobians:
:
Similar formulas can be derived for
Integer programming
Cramer's rule can be used to prove that an
integer programming problem whose constraint matrix is
totally unimodular and whose right-hand side is integer, has integer basic solutions. This makes the integer program substantially easier to solve.
Ordinary differential equations
Cramer's rule is used to derive the general solution to an inhomogeneous linear differential equation by the method of
variation of parameters.
Example
Consider the linear system
:
Applying Cramer's Rule gives
:
These values can be verified by substituting back into the original equations:
and
as required.
Geometric interpretation

Cramer's rule has a geometric interpretation that can be considered also a proof or simply giving insight about its geometric nature. These geometric arguments work in general and not only in the case of two equations with two unknowns presented here.
Given the system of equations
:
it can be considered as an equation between vectors
:
The area of the parallelogram determined by
and
is given by the determinant of the system of equations:
:
In general, when there are more variables and equations, the determinant of vectors of length will give the ''volume'' of the ''
parallelepiped
In geometry, a parallelepiped is a three-dimensional figure formed by six parallelograms (the term ''rhomboid'' is also sometimes used with this meaning). By analogy, it relates to a parallelogram just as a cube relates to a square.
Three equiva ...
'' determined by those vectors in the -th dimensional
Euclidean space
Euclidean space is the fundamental space of geometry, intended to represent physical space. Originally, in Euclid's ''Elements'', it was the three-dimensional space of Euclidean geometry, but in modern mathematics there are ''Euclidean spaces ...
.
Therefore, the area of the parallelogram determined by
and
has to be
times the area of the first one since one of the sides has been multiplied by this factor. Now, this last parallelogram, by
Cavalieri's principle, has the same area as the parallelogram determined by
and
Equating the areas of this last and the second parallelogram gives the equation
:
from which Cramer's rule follows.
Other proofs
A proof by abstract linear algebra
This is a restatement of the proof above in abstract language.
Consider the map
where
is the matrix
with
substituted in the
th column, as in Cramer's rule. Because of linearity of determinant in every column, this map is linear. Observe that it sends the
th column of
to the
th basis vector
(with 1 in the
th place), because determinant of a matrix with a repeated column is 0. So we have a linear map which agrees with the inverse of
on the column space; hence it agrees with
on the span of the column space. Since
is invertible, the column vectors span all of
, so our map really is the inverse of
. Cramer's rule follows.
A short proof
A short proof of Cramer's rule
can be given by noticing that
is the determinant of the matrix
:
On the other hand, assuming that our original matrix is invertible, this matrix
has columns
, where
is the ''n''-th column of the matrix . Recall that the matrix
has columns
, and therefore
. Hence, by using that the determinant of the product of two matrices is the product of the determinants, we have
:
The proof for other
is similar.
Using Geometric Algebra
Inconsistent and indeterminate cases
A system of equations is said to be
inconsistent when there are no solutions and it is called
indeterminate when there is more than one solution. For linear equations, an indeterminate system will have infinitely many solutions (if it is over an infinite field), since the solutions can be expressed in terms of one or more parameters that can take arbitrary values.
Cramer's rule applies to the case where the coefficient determinant is nonzero. In the 2×2 case, if the coefficient determinant is zero, then the system is inconsistent if the numerator determinants are nonzero, or indeterminate if the numerator determinants are zero.
For 3×3 or higher systems, the only thing one can say when the coefficient determinant equals zero is that if any of the numerator determinants are nonzero, then the system must be inconsistent. However, having all determinants zero does not imply that the system is indeterminate. A simple example where all determinants vanish (equal zero) but the system is still inconsistent is the 3×3 system ''x''+''y''+''z''=1, ''x''+''y''+''z''=2, ''x''+''y''+''z''=3.
See also
*
Rouché–Capelli theorem
Rouché–Capelli theorem is a theorem in linear algebra that determines the number of solutions of a system of linear equations, given the ranks of its augmented matrix and coefficient matrix. The theorem is variously known as the:
* Rouché� ...
*
Gaussian elimination
In mathematics, Gaussian elimination, also known as row reduction, is an algorithm for solving systems of linear equations. It consists of a sequence of row-wise operations performed on the corresponding matrix of coefficients. This method can a ...
References
External links
Proof of Cramer's Rule
WebApp descriptively solving systems of linear equations with Cramer's RuleOnline Calculator of System of linear equations
{{DEFAULTSORT:Cramer's Rule
Theorems in linear algebra
Determinants
1750 in science