In linear algebra, the **determinant** is a function that associates a scalar to every square matrix. For instance, the 2-by-2 matrix

- .

The determinant of *A* is also sometimes denoted by |*A*|, but this notation should be avoided as it is also used to denote other matrix functions, such as the square root of *AA*^{*}.

Table of contents |

2 Definition and Computation 3 Properties 4 Generalizations |

Historically, determinants were considered before matrices. Originally, a determinant was defined as a property of a system of linear equations. The determinant "determines" whether the system has a unique solution (which occurs precisely if the determinant is non-zero). In this sense, two-by-two determinants were considered by Cardano at the end of the 16th century and larger ones by Leibniz about 100 years later.

Determinants are used to characterize invertible matrices, and to explicitly describe the solution to a system of linear equations with Cramer's rule. It can be used to find the eigenvalues of the matrix *A* through the characteristic polynomial *p*(*x*) = det(*A*-*xI*_{n}).

One often thinks of the determinant as assigning a number to every sequence of *n* vectors in **R**^{n}, by using the square matrix whose columns are the given vectors.
With this understanding, the sign of the determinant of a basis can be used to define the notion of orientation in Euclidean spaces.

Determinants are used to calculate volumes in vector calculus: the absolute value of the determinant of real vectors is equal to the volume of the parallelepiped spanned by those vectors. As a consequence, if the linear map *f* : **R**^{n} `->` **R**^{n} is represented by the matrix *A*, and *S* is any measurable subset of **R**^{n}, then the volume of *f*(*S*) is given by |det(*A*)| × volume(*S*). More generally, if the linear map *f* : **R**^{n} `->` **R**^{m} is represented by the *m*-by-*n* matrix *A*, and *S* is any measurable subset of **R**^{n}, then the *n*-dimensional volume of *f*(*S*) is given by √(det(*A*^{T}*A*)) × volume(*S*).

Suppose *A* = (*A*_{i,j}) is a square matrix.

This formula contains *n* summands and is therefore impractical to use if *n* is bigger than 3.

In general, determinants can be computed with the Gauss algorithm using the following rules:

- If
*A*is a triangular matrix, i.e.*A*_{i,j}= 0 whenver*i*>*j*, then det(*A*) =*A*_{1,1}·*A*_{2,2}·...·*A*_{n,n} - If
*B*results from*A*by exchanging two rows or columns, then det(*B*) = - det(*A*) - If
*B*results from*A*by multiplying one row or column with the number*c*, then det(*B*) =*c*· det(*A*) - If
*B*results from*A*by adding a multiple of one row or column to another row or column, then det(*B*) = det(*A*).

It is also possible to expand a determinant along a row or column using *Laplace's formula*, which is efficient for relatively small matrices. To do this along row *i*, say, we write

The determinant is a *multiplicative map* in the sense that

It is easy to see that det(*rI*_{n})=*r*^{n} and thus

- det(
*rA*) =*r*^{n}det(*A*) for all*n*-by-*n*matrices*A*and all scalars*r*.

- det(
*A*^{-1})=det(*A*)^{-1}.

- det(
*A*) = det(*A*^{T}).

- det(
*A*) = det(*B*).

There exist matrices which have the same determinant but are not similar.

If *A* is a square *n*-by-*n* matrix with real or complex entries and if λ_{1},...,λ_{n} are the (complex) eigenvalues of *A* listed according to their algebraic multiplicities, then

- det(
*A*) = λ_{1}·λ_{2}·...·λ_{n}.

From the connection between the determinant and the eigenvalues, one can derive a connection between the trace function, the exponential function, and the determinant:

- det(exp(
*A*)) = exp(tr(*A*)).

The determinant of real square matrices is a polynomial function from **R**^{n×n} to **R**, and as such is everywhere differentiable. Its derivative can be expressed using *Jacobi's formula*:

- d det(
*A*) = tr(adj(*A*) d*A*)

- d det(
*A*) = det(*A*) tr(*A*^{-1}d*A*)

- det(
*A*+*X*) - det(*A*) ≈ det(*A*) tr(*A*^{-1}*X*)

- det(
*I*+*X*) ≈ 1 + tr(*X*).

It makes sense to define the determinant for matrices whose entries come from any commutative ring. The computation rules, the Leibniz formula and the compatibility with matrix multiplication remain valid, except that now a matrix *A* is invertible if and only if det(*A*) is an invertible element of the ground ring.

Abstractly, one may define the determinant as a certain anti-symmetric multilinear map as follows: if *R* is a commutative ring and *M* = *R*^{n} denotes the free *R*-module with *n* generators, then

- det :
*M*^{n}`->`*R*

- det is
*R*-linear in each of the*n*arguments. - det is anti-symmetric, meaning that if two of the
*n*arguments are equal, then the determinant is zero. - det(
*e*_{1},..,*e*_{n}) = 1, where*e*_{i}is that element of*M*which has a 1 in the*i*-th coordinate and zeros elsewhere.