determinant

In mathematics, the determinant is a scalar value that is a function of the entries of a square matrix. It characterizes some properties of the matrix and the linear map represented by the matrix. In particular, the determinant is nonzero if and only if the matrix is invertible and the linear map represented by the matrix is an isomorphism. The determinant of a product of matrices is the product of their determinants (the preceding property is a corollary of this one).
The determinant of a matrix is denoted , , or .

The determinant of a matrix is
:$\begin a & b\\c & d \end=ad-bc,$
and the determinant of a matrix is
:$\begin a & b & c \\ d & e & f \\ g & h & i \end= aei + bfg + cdh - ceg - bdi - afh.$

The determinant of a matrix can be defined in several equivalent ways. Leibniz formula expresses the determinant as a sum of signed products of matrix entries such that each summand is the product of different entries, and the number of these summands is $n!,$ the factorial of (the product of the first positive integers). The Laplace expansion expresses the determinant of a matrix as a linear combination of determinants of $(n-1)\times(n-1)$ submatrices. Gaussian elimination express the determinant as the product of the diagonal entries of a diagonal matrix that is obtained by a succession of elementary row operations.

Determinants can also be defined by some of their properties: the determinant is the unique function defined on the matrices that has the four following properties. The determinant of the identity matrix is ; the exchange of two rows (or of two columns) multiplies the determinant by ; multiplying a row (or a column) by a number multiplies the determinant by this number; and adding to a row (or a column) a multiple of another row (or column) does not change the determinant.

Determinants occur throughout mathematics. For example, a matrix is often used to represent the coefficients in a system of linear equations, and determinants can be used to solve these equations ( Cramer's rule), although other methods of solution are computationally much more efficient. Determinants are used for defining the characteristic polynomial of a matrix, whose roots are the eigenvalues. In geometry, the signed -dimensional volume of a -dimensional parallelepiped is expressed by a determinant. This is used in calculus with exterior differential forms and the Jacobian determinant, in particular for changes of variables in multiple integrals.

2 × 2 matrices

The determinant of a matrix $\begin a & b \\c & d \end$ is denoted either by "" or by vertical bars around the matrix, and is defined as
:$\det \begin a & b \\c & d \end = \begin a & b \\c & d \end = ad - bc.$
For example,
:$\det \begin 3 & 7 \\1 & -4 \end = \begin 3 & 7 \\ 1 & \end = 3 \cdot (-4) - 7 \cdot 1 = -19.$

First properties

The determinant has several key properties that can be proved by direct evaluation of the definition for $2 \times 2$-matrices, and that continue to hold for determinants of larger matrices. They are as follows: first, the determinant of the identity matrix $\begin1 & 0 \\ 0 & 1 \end$ is 1.
Second, the determinant is zero if two rows are the same:
:$\begin a & b \\ a & b \end = ab - ba = 0.$
This holds similarly if the two columns are the same. Moreover,
:$\begina & b + b' \\ c & d + d' \end = a(d+d')-(b+b')c = \begina & b\\ c & d \end + \begina & b' \\ c & d' \end.$
Finally, if any column is multiplied by some number $r$ (i.e., all entries in that column are multiplied by that number), the determinant is also multiplied by that number:
:$\begin r \cdot a & b \\ r \cdot c & d \end = rad - brc = r(ad-bc) = r \cdot \begin a & b \\c & d \end.$

Geometric meaning

If the matrix entries are real numbers, the matrix can be used to represent two linear maps: one that maps the standard basis vectors to the rows of , and one that maps them to the columns of . In either case, the images of the basis vectors form a parallelogram that represents the image of the unit square under the mapping. The parallelogram defined by the rows of the above matrix is the one with vertices at , , , and , as shown in the accompanying diagram.

The absolute value of is the area of the parallelogram, and thus represents the scale factor by which areas are transformed by . (The parallelogram formed by the columns of is in general a different parallelogram, but since the determinant is symmetric with respect to rows and columns, the area will be the same.)

The absolute value of the determinant together with the sign becomes the ''oriented area'' of the parallelogram. The oriented area is the same as the usual area, except that it is negative when the angle from the first to the second vector defining the parallelogram turns in a clockwise direction (which is opposite to the direction one would get for the identity matrix).

To show that is the signed area, one may consider a matrix containing two vectors and representing the parallelogram's sides. The signed area can be expressed as for the angle ''θ'' between the vectors, which is simply base times height, the length of one vector times the perpendicular component of the other. Due to the sine this already is the signed area, yet it may be expressed more conveniently using the cosine of the complementary angle to a perpendicular vector, e.g. , so that , which can be determined by the pattern of the scalar product to be equal to :

: $\text = , \boldsymbol, \,, \boldsymbol, \,\sin\,\theta = \left, \boldsymbol^\perp\\,\left, \boldsymbol\\,\cos\,\theta' = \begin -b \\ a \end \cdot \begin c \\ d \end = ad - bc.$

Thus the determinant gives the scaling factor and the orientation induced by the mapping represented by ''A''. When the determinant is equal to one, the linear mapping defined by the matrix is equi-areal and orientation-preserving.

The object known as the '' bivector'' is related to these ideas. In 2D, it can be interpreted as an ''oriented plane segment'' formed by imagining two vectors each with origin , and coordinates and . The bivector magnitude (denoted by ) is the ''signed area'', which is also the determinant .

If an real matrix ''A'' is written in terms of its column vectors A = \left begin \mathbf_1 & \mathbf_2 & \cdots & \mathbf_n\end\right/math>, then
:$A\begin1 \\ 0\\ \vdots \\0\end = \mathbf_1, \quad A\begin0 \\ 1\\ \vdots \\0\end = \mathbf_2, \quad \ldots, \quad A\begin0 \\0 \\ \vdots \\1\end = \mathbf_n.$

This means that $A$ maps the unit ''n''-cube to the ''n''-dimensional parallelotope defined by the vectors $\mathbf_1, \mathbf_2, \ldots, \mathbf_n,$ the region $P = \left\.$

The determinant gives the signed ''n''-dimensional volume of this parallelotope, $\det(A) = \pm \text(P),$ and hence describes more generally the ''n''-dimensional volume scaling factor of the linear transformation produced by ''A''. (The sign shows whether the transformation preserves or reverses orientation.) In particular, if the determinant is zero, then this parallelotope has volume zero and is not fully ''n''-dimensional, which indicates that the dimension of the image of ''A'' is less than ''n''. This means that ''A'' produces a linear transformation which is neither onto nor one-to-one, and so is not invertible.

Definition

In the sequel, ''A'' is a square matrix with ''n'' rows and ''n'' columns, so that it can be written as
:$A = \begin a_ & a_ & \cdots & a_ \\ a_ & a_ & \cdots & a_ \\ \vdots & \vdots & \ddots & \vdots \\ a_ & a_ & \cdots & a_ \end.$

The entries $a_$ etc. are, for many purposes, real or complex numbers. As discussed below, the determinant is also defined for matrices whose entries are in a commutative ring.

The determinant of ''A'' is denoted by det(''A''), or it can be denoted directly in terms of the matrix entries by writing enclosing bars instead of brackets:
:$\begin a_ & a_ & \cdots & a_ \\ a_ & a_ & \cdots & a_ \\ \vdots & \vdots & \ddots & \vdots \\ a_ & a_ & \cdots & a_ \end.$

There are various equivalent ways to define the determinant of a square matrix ''A'', i.e. one with the same number of rows and columns: the determinant can be defined via the Leibniz formula, an explicit formula involving sums of products of certain entries of the matrix. The determinant can also be characterized as the unique function depending on the entries of the matrix satisfying certain properties. This approach can also be used to compute determinants by simplifying the matrices in question.

Leibniz formula

3 × 3 matrices

The ''Leibniz formula'' for the determinant of a matrix is the following:
:$\begin \begina&b&c\\d&e&f\\g&h&i\end &= a(ei - fh) - b(di - fg) + c(dh - eg) \\ &= aei + bfg + cdh - ceg - bdi - afh. \end$

The rule of Sarrus is a mnemonic for the expanded form of this determinant: the sum of the products of three diagonal north-west to south-east lines of matrix elements, minus the sum of the products of three diagonal south-west to north-east lines of elements, when the copies of the first two columns of the matrix are written beside it as in the illustration. This scheme for calculating the determinant of a matrix does not carry over into higher dimensions.

''n'' × ''n'' matrices

In higher dimension, the Leibniz formula expresses the determinant of an $n \times n$-matrix as an expression involving permutations and their '' signatures''. A permutation of the set $\$ is a function $\sigma$ that reorders this set of integers. The value in the $i$-th position after the reordering $\sigma$ is denoted below by $\sigma_i$. The set of all such permutations, called the symmetric group, is commonly denoted $S_n$. The signature $\sgn(\sigma)$ of a permutation $\sigma$ is $+1,$ if the permutation can be obtained with an even number of exchanges of two entries; otherwise, it is $-1.$

Given a matrix
:$A=\begin a_\ldots a_\\ \vdots\qquad\vdots\\ a_\ldots a_ \end,$
the Leibniz formula for its determinant is, using sigma notation,
:$\det(A)=\begin a_\ldots a_\\ \vdots\qquad\vdots\\ a_\ldots a_ \end = \sum_\sgn(\sigma)a_\cdots a_.$

Using pi notation, this can be shortened into
:$\det(A) = \sum_ \left( \sgn(\sigma) \prod_^n a_\right)$.

The Levi-Civita symbol $\varepsilon_$ is defined on the -tuples of integers in $\$ as if two of the integers are equal, and, otherwise, as the signature of the permutation defined by the tuple of integers. With the Levi-Civita symbol, Leibniz formula may be written as
:$\det(A) = \sum_ \varepsilon_ a_ \cdots a_,$
where the sum is taken over all -tuples of integers in $\.$

Properties of the determinant

Characterization of the determinant

The determinant can be characterized by the following three key properties. To state these, it is convenient to regard an $n \times n$-matrix ''A'' as being composed of its $n$ columns, so denoted as
:$A = \big ( a_1, \dots, a_n \big ),$
where the column vector $a_i$ (for each ''i'') is composed of the entries of the matrix in the ''i''-th column.

#

$\det\left(I\right) = 1$, where $I$ is an identity matrix.
#

The determinant is '' multilinear'': if the ''j''th column of a matrix $A$ is written as a linear combination $a_j = r \cdot v + w$ of two column vectors ''v'' and ''w'' and a number ''r'', then the determinant of ''A'' is expressible as a similar linear combination:
#: $\begin, A, &= \big , a_1, \dots, a_, r \cdot v + w, a_, \dots, a_n , \\ &= r \cdot , a_1, \dots, v, \dots a_n , + , a_1, \dots, w, \dots, a_n , \end$
#

The determinant is ''alternating'': whenever two columns of a matrix are identical, its determinant is 0:
#: $, a_1, \dots, v, \dots, v, \dots, a_n, = 0.$

If the determinant is defined using the Leibniz formula as above, these three properties can be proved by direct inspection of that formula. Some authors also approach the determinant directly using these three properties: it can be shown that there is exactly one function that assigns to any $n \times n$-matrix ''A'' a number that satisfies these three properties. This also shows that this more abstract approach to the determinant yields the same definition as the one using the Leibniz formula.

To see this it suffices to expand the determinant by multi-linearity in the columns into a (huge) linear combination of determinants of matrices in which each column is a standard basis vector. These determinants are either 0 (by property 9) or else ±1 (by properties 1 and 12 below), so the linear combination gives the expression above in terms of the Levi-Civita symbol. While less technical in appearance, this characterization cannot entirely replace the Leibniz formula in defining the determinant, since without it the existence of an appropriate function is not clear.

Immediate consequences

These rules have several further consequences:
* The determinant is a homogeneous function, i.e., $\det(cA) = c^n\det(A)$ (for an $n \times n$ matrix $A$).
* Interchanging any pair of columns of a matrix multiplies its determinant by −1. This follows from the determinant being multilinear and alternating (properties 2 and 3 above): $, a_1, \dots, a_j, \dots a_i, \dots, a_n, = - , a_1, \dots, a_i, \dots, a_j, \dots, a_n, .$ This formula can be applied iteratively when several columns are swapped. For example $, a_3, a_1, a_2, a_4 \dots, a_n, = - , a_1, a_3, a_2, a_4, \dots, a_n, = , a_1, a_2, a_3, a_4, \dots, a_n, .$ Yet more generally, any permutation of the columns multiplies the determinant by the sign of the permutation.
* If some column can be expressed as a linear combination of the ''other'' columns (i.e. the columns of the matrix form a linearly dependent set), the determinant is 0. As a special case, this includes: if some column is such that all its entries are zero, then the determinant of that matrix is 0.
* Adding a scalar multiple of one column to ''another'' column does not change the value of the determinant. This is a consequence of multilinearity and being alternative: by multilinearity the determinant changes by a multiple of the determinant of a matrix with two equal columns, which determinant is 0, since the determinant is alternating.
* If $A$ is a triangular matrix, i.e. $a_=0$, whenever $i>j$ or, alternatively, whenever i, then its determinant equals the product of the diagonal entries: $\det(A) = a_ a_ \cdots a_ = \prod_^n a_.$ Indeed, such a matrix can be reduced, by appropriately adding multiples of the columns with fewer nonzero entries to those with more entries, to a diagonal matrix (without changing the determinant). For such a matrix, using the linearity in each column reduces to the identity matrix, in which case the stated formula holds by the very first characterizing property of determinants. Alternatively, this formula can also be deduced from the Leibniz formula, since the only permutation $\sigma$ which gives a non-zero contribution is the identity permutation.

Example

These characterizing properties and their consequences listed above are both theoretically significant, but can also be used to compute determinants for concrete matrices. In fact, Gaussian elimination can be applied to bring any matrix into upper triangular form, and the steps in this algorithm affect the determinant in a controlled way. The following concrete example illustrates the computation of the determinant of the matrix $A$ using that method:

:$A = \begin -2 & -1 & 2 \\ 2 & 1 & 4 \\ -3 & 3 & -1 \end.$

Combining these equalities gives $, A, = -, E, = -(18 \cdot 3 \cdot (-1)) = 54.$

Transpose

The determinant of the transpose of $A$ equals the determinant of ''A'':
:$\det\left(A^\textsf\right) = \det(A)$.
This can be proven by inspecting the Leibniz formula. This implies that in all the properties mentioned above, the word "column" can be replaced by "row" throughout. For example, viewing an matrix as being composed of ''n'' rows, the determinant is an ''n''-linear function.

Multiplicativity and matrix groups

The determinant is a ''multiplicative map'', i.e., for square matrices $A$ and $B$ of equal size, the determinant of a matrix product equals the product of their determinants:
:$\det(AB) = \det (A) \det (B)$

This key fact can be proven by observing that, for a fixed matrix $B$, both sides of the equation are alternating and multilinear as a function depending on the columns of $A$. Moreover, they both take the value $\det B$ when $A$ is the identity matrix. The above-mentioned unique characterization of alternating multilinear maps therefore shows this claim.

A matrix $A$ is invertible precisely if its determinant is nonzero. This follows from the multiplicativity of $\det$ and the formula for the inverse involving the adjugate matrix mentioned below. In this event, the determinant of the inverse matrix is given by
:\det\left(A^\right) = \frac = det(A).

In particular, products and inverses of matrices with non-zero determinant (respectively, determinant one) still have this property. Thus, the set of such matrices (of fixed size $n$) forms a group known as the general linear group $\operatorname_n$ (respectively, a subgroup called the special linear group $\operatorname_n \subset \operatorname_n$. More generally, the word "special" indicates the subgroup of another matrix group of matrices of determinant one. Examples include the special orthogonal group (which if ''n'' is 2 or 3 consists of all rotation matrices), and the special unitary group.

The Cauchy–Binet formula is a generalization of that product formula for ''rectangular'' matrices. This formula can also be recast as a multiplicative formula for compound matrices whose entries are the determinants of all quadratic submatrices of a given matrix.

Laplace expansion

Laplace expansion expresses the determinant of a matrix $A$ in terms of determinants of smaller matrices, known as its minors. The minor $M_$ is defined to be the determinant of the $(n-1) \times (n-1)$-matrix that results from $A$ by removing the $i$-th row and the $j$-th column. The expression $(-1)^M_$ is known as a cofactor. For every $i$, one has the equality
:$\det(A) = \sum_^n (-1)^ a_ M_,$
which is called the ''Laplace expansion along the th row''. For example, the Laplace expansion along the first row ($i=1$) gives the following formula:
:$\begina&b&c\\ d&e&f\\ g&h&i\end = a\begine&f\\ h&i\end - b\begind&f\\ g&i\end + c\begind&e\\ g&h\end$
Unwinding the determinants of these $2 \times 2$-matrices gives back the Leibniz formula mentioned above. Similarly, the ''Laplace expansion along the $j$-th column'' is the equality
:$\det(A)= \sum_^n (-1)^ a_ M_.$
Laplace expansion can be used iteratively for computing determinants, but this approach is inefficient for large matrices. However, it is useful for computing the determinants of highly symmetric matrix such as the Vandermonde matrix
$\begin 1 & 1 & 1 & \cdots & 1 \\ x_1 & x_2 & x_3 & \cdots & x_n \\ x_1^2 & x_2^2 & x_3^2 & \cdots & x_n^2 \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ x_1^ & x_2^ & x_3^ & \cdots & x_n^ \end = \prod_ \left(x_j - x_i\right).$
This determinant has been applied, for example, in the proof of Baker's theorem in the theory of transcendental numbers.

Adjugate matrix

The adjugate matrix $\operatorname(A)$ is the transpose of the matrix of the cofactors, that is,
: $(\operatorname(A))_ = (-1)^ M_.$

For every matrix, one has
: $(\det A) I = A\operatornameA = (\operatornameA)\,A.$

Thus the adjugate matrix can be used for expressing the inverse of a nonsingular matrix:
: $A^ = \frac 1\operatornameA.$

Block matrices

The formula for the determinant of a $2 \times 2$-matrix above continues to hold, under appropriate further assumptions, for a block matrix, i.e., a matrix composed of four submatrices $A, B, C, D$ of dimension $n \times n$, $n \times m$, $m \times n$ and $m \times m$, respectively. The easiest such formula, which can be proven using either the Leibniz formula or a factorization involving the Schur complement, is
:$\det\beginA& 0\\ C& D\end = \det(A) \det(D) = \det\beginA& B\\ 0& D\end.$
If $A$ is invertible (and similarly if $D$ is invertible), one has

:$\det\beginA& B\\ C& D\end = \det(A) \det\left(D - C A^ B\right) .$
If $D$ is a $1 \times 1$-matrix, this simplifies to $\det (A) (D - CA^B)$.

If the blocks are square matrices of the ''same'' size further formulas hold. For example, if $C$ and $D$ commute (i.e., $CD=DC$), then there holds

:$\det\beginA& B\\ C& D\end = \det(AD - BC).$
This formula has been generalized to matrices composed of more than $2 \times 2$ blocks, again under appropriate commutativity conditions among the individual blocks.

For $A = D$ and $B = C$, the following formula holds (even if $A$ and $B$ do not commute)
:$\det\beginA& B\\ B& A\end = \det(A - B) \det(A + B).$

Sylvester's determinant theorem

Sylvester's determinant theorem states that for ''A'', an matrix, and ''B'', an matrix (so that ''A'' and ''B'' have dimensions allowing them to be multiplied in either order forming a square matrix):

:$\det\left(I_\mathit + AB\right) = \det\left(I_\mathit + BA\right),$

where ''I''_''m'' and ''I''_''n'' are the and identity matrices, respectively.

From this general result several consequences follow.

Sum

The determinant of the sum $A+B$ of two square matrices of the same size is not in general expressible in terms of the determinants of ''A'' and of ''B''. However, for positive semidefinite matrices $A$, $B$ and $C$ of equal size, $\det(A + B + C) + \det(C) \geq \det(A + C) + \det(B + C)\text$ with the corollary $\det(A + B) \geq \det(A) + \det(B)\text$ Conversely, if $A$ and $B$ are Hermitian, positive-definite, and size $n\times n$, then the determinant has concave $n$^th root; this implies \sqrt geq\sqrt \sqrt /math> by homogeneity.

Sum identity for 2×2 matrices

For the special case of $2\times 2$ matrices with complex entries, the determinant of the sum can be written in terms of determinants and traces in the following identity:
:$\det(A+B) = \det(A) + \det(B) + \text(A)\text(B) - \text(AB).$

This has an application to $2\times 2$ matrix algebras. For example, consider the complex numbers as a matrix algebra. The complex numbers have a representation as matrices of the form
$aI + b\mathbf := a\begin 1 & 0 \\ 0 & 1 \end + b\begin 0 & -1 \\ 1 & 0 \end$
with $a$ and $b$ real. Since $\text(\mathbf) = 0$, taking $A = aI$ and $B = b\mathbf$ in the above identity gives
:$\det(aI + b\mathbf) = a^2\det(I) + b^2\det(\mathbf) = a^2 + b^2.$
This result followed just from $\text(\mathbf) = 0$ and $\det(I) = \det(\mathbf) = 1$.

Properties of the determinant in relation to other notions

Eigenvalues and characteristic polynomial

The determinant is closely related to two other central concepts in linear algebra, the eigenvalues and the characteristic polynomial of a matrix. Let $A$ be an $n \times n$-matrix with complex entries with eigenvalues $\lambda_1, \lambda_2, \ldots, \lambda_n$. (Here it is understood that an eigenvalue with algebraic multiplicity occurs times in this list.) Then the determinant of is the product of all eigenvalues,
:$\det(A) = \prod_^n \lambda_i=\lambda_1\lambda_2\cdots\lambda_n.$
The product of all non-zero eigenvalues is referred to as pseudo-determinant.

The characteristic polynomial is defined as
:$\chi_A(t) = \det(t \cdot I - A).$
Here, $t$ is the indeterminate of the polynomial and $I$ is the identity matrix of the same size as $A$. By means of this polynomial, determinants can be used to find the eigenvalues of the matrix $A$: they are precisely the roots of this polynomial, i.e., those complex numbers $\lambda$ such that
:$\chi_A(\lambda) = 0.$

A Hermitian matrix is positive definite if all its eigenvalues are positive. Sylvester's criterion asserts that this is equivalent to the determinants of the submatrices
:$A_k := \begin a_ & a_ & \cdots & a_ \\ a_ & a_ & \cdots & a_ \\ \vdots & \vdots & \ddots & \vdots \\ a_ & a_ & \cdots & a_ \end$

being positive, for all $k$ between $1$ and $n$.

Trace

The trace tr(''A'') is by definition the sum of the diagonal entries of and also equals the sum of the eigenvalues. Thus, for complex matrices ,
:$\det(\exp(A)) = \exp(\operatorname(A))$

or, for real matrices ,
:$\operatorname(A) = \log(\det(\exp(A))).$

Here exp() denotes the matrix exponential of , because every eigenvalue of corresponds to the eigenvalue exp() of exp(). In particular, given any logarithm of , that is, any matrix satisfying
:$\exp(L) = A$

the determinant of is given by
:$\det(A) = \exp(\operatorname(L)).$

For example, for , , and , respectively,
:$\begin \det(A) &= \frac\left(\left(\operatorname(A)\right)^2 - \operatorname\left(A^2\right)\right), \\ \det(A) &= \frac\left(\left(\operatorname(A)\right)^3 - 3\operatorname(A) ~ \operatorname\left(A^2\right) + 2 \operatorname\left(A^3\right)\right), \\ \det(A) &= \frac\left(\left(\operatorname(A)\right)^4 - 6\operatorname\left(A^2\right)\left(\operatorname(A)\right)^2 + 3\left(\operatorname\left(A^2\right)\right)^2 + 8\operatorname\left(A^3\right)~\operatorname(A) - 6\operatorname\left(A^4\right)\right). \end$

cf. Cayley-Hamilton theorem. Such expressions are deducible from combinatorial arguments, Newton's identities, or the Faddeev–LeVerrier algorithm. That is, for generic , the signed constant term of the characteristic polynomial, determined recursively from
:$c_n = 1; ~~~c_ = -\frac\sum_^m c_ \operatorname\left(A^k\right) ~~(1 \le m \le n)~.$

In the general case, this may also be obtained from
:$\det(A) = \sum_\prod_^n \frac \operatorname\left(A^l\right)^,$

where the sum is taken over the set of all integers satisfying the equation
:$\sum_^n lk_l = n.$

The formula can be expressed in terms of the complete exponential Bell polynomial of ''n'' arguments ''s''_''l'' = −(''l'' – 1)! tr(''A''^''l'') as
:$\det(A) = \frac B_n(s_1, s_2, \ldots, s_n).$

This formula can also be used to find the determinant of a matrix with multidimensional indices and . The product and trace of such matrices are defined in a natural way as
:$(AB)^I_J = \sum_K A^I_K B^K_J, \operatorname(A) = \sum_I A^I_I.$

An important arbitrary dimension identity can be obtained from the Mercator series expansion of the logarithm when the expansion converges. If every eigenvalue of ''A'' is less than 1 in absolute value,
:$\det(I + A) = \sum_^\infty \frac \left(-\sum_^\infty \frac \operatorname\left(A^j\right)\right)^k\,,$

where is the identity matrix. More generally, if
:$\sum_^\infty \frac \left(-\sum_^\infty \frac\operatorname\left(A^j\right)\right)^k\,,$

is expanded as a formal power series in then all coefficients of for are zero and the remaining polynomial is .

Upper and lower bounds

For a positive definite matrix , the trace operator gives the following tight lower and upper bounds on the log determinant
:$\operatorname\left(I - A^\right) \le \log\det(A) \le \operatorname(A - I)$

with equality if and only if . This relationship can be derived via the formula for the Kullback-Leibler divergence between two multivariate normal distributions.

Also,
:$\frac \leq \det(A)^\frac \leq \frac\operatorname(A) \leq \sqrt.$

These inequalities can be proved by expressing the traces and the determinant in terms of the eigenvalues. As such, they represent the well-known fact that the harmonic mean is less than the geometric mean, which is less than the arithmetic mean, which is, in turn, less than the root mean square.

Derivative

The Leibniz formula shows that the determinant of real (or analogously for complex) square matrices is a polynomial function from $\mathbf R^$ to $\mathbf R$. In particular, it is everywhere differentiable. Its derivative can be expressed using Jacobi's formula:

:$\frac = \operatorname\left(\operatorname(A) \frac\right).$

where $\operatorname(A)$ denotes the adjugate of $A$. In particular, if $A$ is invertible, we have

:$\frac = \det(A) \operatorname\left(A^ \frac\right).$

Expressed in terms of the entries of $A$, these are

: $\frac= \operatorname(A)_ = \det(A)\left(A^\right)_.$

Yet another equivalent formulation is

:$\det(A + \epsilon X) - \det(A) = \operatorname(\operatorname(A) X) \epsilon + O\left(\epsilon^2\right) = \det(A) \operatorname\left(A^ X\right) \epsilon + O\left(\epsilon^2\right)$,

using big O notation. The special case where $A = I$, the identity matrix, yields

:$\det(I + \epsilon X) = 1 + \operatorname(X) \epsilon + O\left(\epsilon^2\right).$

This identity is used in describing Lie algebras associated to certain matrix Lie groups. For example, the special linear group $\operatorname_n$ is defined by the equation $\det A = 1$. The above formula shows that its Lie algebra is the special linear Lie algebra $\mathfrak_n$ consisting of those matrices having trace zero.

Writing a $3 \times 3$-matrix as $A = \begina & b & c\end$ where $a, b,c$ are column vectors of length 3, then the gradient over one of the three vectors may be written as the cross product of the other two:

: $\begin \nabla_\mathbf\det(A) &= \mathbf \times \mathbf \\ \nabla_\mathbf\det(A) &= \mathbf \times \mathbf \\ \nabla_\mathbf\det(A) &= \mathbf \times \mathbf. \end$

History

Historically, determinants were used long before matrices: A determinant was originally 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, determinants were first used in the Chinese mathematics textbook '' The Nine Chapters on the Mathematical Art'' (九章算術, Chinese scholars, around the 3rd century BCE). In Europe, solutions of linear systems of two equations were expressed by Cardano in 1545 by a determinant-like entity.

Determinants proper originated from the work of Seki Takakazu in 1683 in Japan and parallelly of Leibniz in 1693. stated, without proof, Cramer's rule. Both Cramer and also were led to determinants by the question of plane curves passing through a given set of points.

Vandermonde (1771) first recognized determinants as independent functions.Campbell, H: "Linear Algebra With Applications", pages 111–112. Appleton Century Crofts, 1971 gave the general method of expanding a determinant in terms of its complementary minors: Vandermonde had already given a special case. Immediately following, Lagrange

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