In

$\backslash det\backslash left(I\backslash right)\; =\; 1$, 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 $a\_j\; =\; r\; \backslash cdot\; v\; +\; w$ of two column vectors ''v'' and ''w'' and a number ''r'', then the determinant of ''A'' is expressible as a similar linear combination:
#: $\backslash begin,\; A,\; \&=\; \backslash big\; ,\; a\_1,\; \backslash dots,\; a\_,\; r\; \backslash cdot\; v\; +\; w,\; a\_,\; \backslash dots,\; a\_n\; ,\; \backslash \backslash \; \&=\; r\; \backslash cdot\; ,\; a\_1,\; \backslash dots,\; v,\; \backslash dots\; a\_n\; ,\; +\; ,\; a\_1,\; \backslash dots,\; w,\; \backslash dots,\; a\_n\; ,\; \backslash end$
# The determinant is '' alternating'': whenever two columns of a matrix are identical, its determinant is 0:
#: $,\; a\_1,\; \backslash dots,\; v,\; \backslash dots,\; v,\; \backslash dots,\; a\_n,\; =\; 0.$
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 $n\; \backslash times\; n$-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

_{''m''} and ''I''_{''n''} are the and identity matrices, respectively.
From this general result several consequences follow.

^{th} root; this implies $$\backslash sqrt;\; href="/html/ALL/s/.html"\; ;"title="">$$

_{''l''} = −(''l'' – 1)! tr(''A''^{''l''}) as
:$\backslash det(A)\; =\; \backslash frac\; B\_n(s\_1,\; s\_2,\; \backslash ldots,\; s\_n).$
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
:$(AB)^I\_J\; =\; \backslash sum\_K\; A^I\_K\; B^K\_J,\; \backslash operatorname(A)\; =\; \backslash sum\_I\; A^I\_I.$
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,
:$\backslash det(I\; +\; A)\; =\; \backslash sum\_^\backslash infty\; \backslash frac\; \backslash left(-\backslash sum\_^\backslash infty\; \backslash frac\; \backslash operatorname\backslash left(A^j\backslash right)\backslash right)^k\backslash ,,$
where is the identity matrix. More generally, if
:$\backslash sum\_^\backslash infty\; \backslash frac\; \backslash left(-\backslash sum\_^\backslash infty\; \backslash frac\backslash operatorname\backslash left(A^j\backslash right)\backslash right)^k\backslash ,,$
is expanded as a formal power series in then all coefficients of ^{} for are zero and the remaining polynomial is .

^{''n''}, by using the square matrix whose columns are the given vectors. For instance, an orthogonal matrix with entries in R^{''n''} represents an orthonormal basis in

^{''n''} (the domain of ''f''), the integral over ''f''(''U'') of some other function is given by
:$\backslash int\_\; \backslash phi(\backslash mathbf)\backslash ,\; d\backslash mathbf\; =\; \backslash int\_U\; \backslash phi(f(\backslash mathbf))\; \backslash left,\; \backslash det(\backslash operatornamef)(\backslash mathbf)\backslash \; \backslash ,d\backslash mathbf.$
The Jacobian also occurs in the inverse function theorem.
When applied to the field of

Determinant Interactive Program and Tutorial

Linear algebra: determinants.

Compute determinants of matrices up to order 6 using Laplace expansion you choose.

Determinant Calculator

Calculator for matrix determinants, up to the 8th order.

Determinants explained in an easy fashion in the 4th chapter as a part of a Linear Algebra course.

{{authority control Matrix theory Linear algebra Homogeneous polynomials Algebra

mathematics
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, 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
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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
:$\backslash begin\; a\; \&\; b\backslash \backslash c\; \&\; d\; \backslash end=ad-bc,$
and the determinant of a matrix is
:$\backslash begin\; a\; \&\; b\; \&\; c\; \backslash \backslash \; d\; \&\; e\; \&\; f\; \backslash \backslash \; g\; \&\; h\; \&\; i\; \backslash end=\; aei\; +\; bfg\; +\; cdh\; -\; ceg\; -\; bdi\; -\; afh.$
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
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of (the product of the first positive integers). The Laplace expansion expresses the determinant of a matrix as a linear combination of determinants of $(n-1)\backslash times(n-1)$ submatrices. Gaussian elimination
In mathematics, Gaussian elimination, also known as row reduction, is an algorithm
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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
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, 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 eigenvalue
In linear algebra
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s. In geometry
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, the signed -dimensional volume
Volume is a measure of occupied three-dimensional space. It is often quantified numerically using SI derived units (such as the cubic metre and litre) or by various imperial or US customary units (such as the gallon, quart, cubic inch). ...

of a -dimensional parallelepiped
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is expressed by a determinant. This is used in calculus
Calculus, originally called infinitesimal calculus or "the calculus of infinitesimals", is the mathematical study of continuous change, in the same way that geometry
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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 $\backslash begin\; a\; \&\; b\; \backslash \backslash c\; \&\; d\; \backslash end$ is denoted either by "" or by vertical bars around the matrix, and is defined as :$\backslash det\; \backslash begin\; a\; \&\; b\; \backslash \backslash c\; \&\; d\; \backslash end\; =\; \backslash begin\; a\; \&\; b\; \backslash \backslash c\; \&\; d\; \backslash end\; =\; ad\; -\; bc.$ For example, :$\backslash det\; \backslash begin\; 3\; \&\; 7\; \backslash \backslash 1\; \&\; -4\; \backslash end\; =\; \backslash begin\; 3\; \&\; 7\; \backslash \backslash \; 1\; \&\; \backslash end\; =\; 3\; \backslash cdot\; (-4)\; -\; 7\; \backslash cdot\; 1\; =\; -19.$First properties

The determinant has several key properties that can be proved by direct evaluation of the definition for $2\; \backslash times\; 2$-matrices, and that continue to hold for determinants of larger matrices. They are as follows: first, the determinant of the identity matrix $\backslash begin1\; \&\; 0\; \backslash \backslash \; 0\; \&\; 1\; \backslash end$ is 1. Second, the determinant is zero if two rows are the same: :$\backslash begin\; a\; \&\; b\; \backslash \backslash \; a\; \&\; b\; \backslash end\; =\; ab\; -\; ba\; =\; 0.$ This holds similarly if the two columns are the same. Moreover, :$\backslash begina\; \&\; b\; +\; b\text{'}\; \backslash \backslash \; c\; \&\; d\; +\; d\text{'}\; \backslash end\; =\; a(d+d\text{'})-(b+b\text{'})c\; =\; \backslash begina\; \&\; b\backslash \backslash \; c\; \&\; d\; \backslash end\; +\; \backslash begina\; \&\; b\text{'}\; \backslash \backslash \; c\; \&\; d\text{'}\; \backslash end.$ 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: :$\backslash begin\; r\; \backslash cdot\; a\; \&\; b\; \backslash \backslash \; r\; \backslash cdot\; c\; \&\; d\; \backslash end\; =\; rad\; -\; brc\; =\; r(ad-bc)\; =\; r\; \backslash cdot\; \backslash begin\; a\; \&\; b\; \backslash \backslash c\; \&\; d\; \backslash end.$Geometric meaning

If the matrix entries are real numbers, the matrix can be used to represent twolinear map
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s: one that maps the standard basis
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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
In Euclidean geometry, a parallelogram is a simple (non- self-intersecting) quadrilateral with two pairs of parallel sides. The opposite or facing sides of a parallelogram are of equal length and the opposite angles of a parallelogram are of ...

that represents the image of the unit square
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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
Area is the quantity that expresses the extent of a region on the plane or on a curved surface. The area of a plane region or ''plane area'' refers to the area of a shape or planar lamina, while '' surface area'' refers to the area of an ...

, 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
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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 :
: $\backslash text\; =\; ,\; \backslash boldsymbol,\; \backslash ,,\; \backslash boldsymbol,\; \backslash ,\backslash sin\backslash ,\backslash theta\; =\; \backslash left,\; \backslash boldsymbol^\backslash perp\backslash \backslash ,\backslash left,\; \backslash boldsymbol\backslash \backslash ,\backslash cos\backslash ,\backslash theta\text{'}\; =\; \backslash begin\; -b\; \backslash \backslash \; a\; \backslash end\; \backslash cdot\; \backslash begin\; c\; \backslash \backslash \; d\; \backslash end\; =\; ad\; -\; bc.$
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 $A\; =\; \backslash left;\; href="/html/ALL/s/begin\_\backslash mathbf\_1\_\_\backslash mathbf\_2\_\_\backslash cdots\_\_\backslash mathbf\_n\backslash end\backslash right.html"\; ;"title="begin\; \backslash mathbf\_1\; \backslash mathbf\_2\; \backslash cdots\; \backslash mathbf\_n\backslash end\backslash right">begin\; \backslash mathbf\_1\; \backslash mathbf\_2\; \backslash cdots\; \backslash mathbf\_n\backslash end\backslash right$Definition

In the sequel, ''A'' is a square matrix with ''n'' rows and ''n'' columns, so that it can be written as :$A\; =\; \backslash begin\; a\_\; \&\; a\_\; \&\; \backslash cdots\; \&\; a\_\; \backslash \backslash \; a\_\; \&\; a\_\; \&\; \backslash cdots\; \&\; a\_\; \backslash \backslash \; \backslash vdots\; \&\; \backslash vdots\; \&\; \backslash ddots\; \&\; \backslash vdots\; \backslash \backslash \; a\_\; \&\; a\_\; \&\; \backslash cdots\; \&\; a\_\; \backslash end.$ 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: :$\backslash begin\; a\_\; \&\; a\_\; \&\; \backslash cdots\; \&\; a\_\; \backslash \backslash \; a\_\; \&\; a\_\; \&\; \backslash cdots\; \&\; a\_\; \backslash \backslash \; \backslash vdots\; \&\; \backslash vdots\; \&\; \backslash ddots\; \&\; \backslash vdots\; \backslash \backslash \; a\_\; \&\; a\_\; \&\; \backslash cdots\; \&\; a\_\; \backslash end.$ 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: :$\backslash begin\; \backslash begina\&b\&c\backslash \backslash d\&e\&f\backslash \backslash g\&h\&i\backslash end\; \&=\; a(ei\; -\; fh)\; -\; b(di\; -\; fg)\; +\; c(dh\; -\; eg)\; \backslash \backslash \; \&=\; aei\; +\; bfg\; +\; cdh\; -\; ceg\; -\; bdi\; -\; afh.\; \backslash end$ 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 $n\; \backslash times\; n$-matrix as an expression involvingpermutation
In mathematics, a permutation of a set is, loosely speaking, an arrangement of its members into a sequence
In mathematics, a sequence is an enumerated collection of objects in which repetitions are allowed and order matters. Like a set, ...

s and their '' signatures''. A permutation of the set $\backslash $ is a function $\backslash sigma$ that reorders this set of integers. The value in the $i$-th position after the reordering $\backslash sigma$ is denoted below by $\backslash sigma\_i$. The set of all such permutations, called the symmetric group
In abstract algebra, the symmetric group defined over any set is the group whose elements are all the bijection
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, is commonly denoted $S\_n$. The signature $\backslash sgn(\backslash sigma)$ of a permutation $\backslash sigma$ is $+1,$ if the permutation can be obtained with an even number of exchanges of two entries; otherwise, it is $-1.$
Given a matrix
:$A=\backslash begin\; a\_\backslash ldots\; a\_\backslash \backslash \; \backslash vdots\backslash qquad\backslash vdots\backslash \backslash \; a\_\backslash ldots\; a\_\; \backslash end,$
the Leibniz formula for its determinant is, using sigma notation,
:$\backslash det(A)=\backslash begin\; a\_\backslash ldots\; a\_\backslash \backslash \; \backslash vdots\backslash qquad\backslash vdots\backslash \backslash \; a\_\backslash ldots\; a\_\; \backslash end\; =\; \backslash sum\_\backslash sgn(\backslash sigma)a\_\backslash cdots\; a\_.$
Using pi notation, this can be shortened into
:$\backslash det(A)\; =\; \backslash sum\_\; \backslash left(\; \backslash sgn(\backslash sigma)\; \backslash prod\_^n\; a\_\backslash right)$.
The Levi-Civita symbol $\backslash varepsilon\_$ is defined on the -tuple
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s of integers in $\backslash $ 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
:$\backslash det(A)\; =\; \backslash sum\_\; \backslash varepsilon\_\; a\_\; \backslash cdots\; a\_,$
where the sum is taken over all -tuples of integers in $\backslash .$
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 $n\; \backslash times\; n$-matrix ''A'' as being composed of its $n$ columns, so denoted as :$A\; =\; \backslash big\; (\; a\_1,\; \backslash dots,\; a\_n\; \backslash big\; ),$ where the column vector $a\_i$ (for each ''i'') is composed of the entries of the matrix in the ''i''-th column. #standard basis
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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., $$\backslash det(cA)\; =\; c^n\backslash det(A)$$ (for an $n\; \backslash times\; n$ 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): $$,\; a\_1,\; \backslash dots,\; a\_j,\; \backslash dots\; a\_i,\; \backslash dots,\; a\_n,\; =\; -\; ,\; a\_1,\; \backslash dots,\; a\_i,\; \backslash dots,\; a\_j,\; \backslash dots,\; a\_n,\; .$$ This formula can be applied iteratively when several columns are swapped. For example $$,\; a\_3,\; a\_1,\; a\_2,\; a\_4\; \backslash dots,\; a\_n,\; =\; -\; ,\; a\_1,\; a\_3,\; a\_2,\; a\_4,\; \backslash dots,\; a\_n,\; =\; ,\; a\_1,\; a\_2,\; a\_3,\; a\_4,\; \backslash dots,\; a\_n,\; .$$ 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 $imath>,\; then\; its\; determinant\; equals\; the\; product\; of\; the\; diagonal\; entries:$$\backslash det(A)\; =\; a\_\; a\_\; \backslash cdots\; a\_\; =\; \backslash prod\_^n\; a\_.$$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;\; href="/html/ALL/s/diagonal\_matrix.html"\; ;"title="diagonal\; matrix">diagonal\; matrix$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
In mathematics, Gaussian elimination, also known as row reduction, is an algorithm
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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:
:$A\; =\; \backslash begin\; -2\; \&\; -1\; \&\; 2\; \backslash \backslash \; 2\; \&\; 1\; \&\; 4\; \backslash \backslash \; -3\; \&\; 3\; \&\; -1\; \backslash end.$
Combining these equalities gives $,\; A,\; =\; -,\; E,\; =\; -(18\; \backslash cdot\; 3\; \backslash cdot\; (-1))\; =\; 54.$
Transpose

The determinant of thetranspose
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of $A$ equals the determinant of ''A'':
:$\backslash det\backslash left(A^\backslash textsf\backslash right)\; =\; \backslash det(A)$.
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: :$\backslash det(AB)\; =\; \backslash det\; (A)\; \backslash det\; (B)$ 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 $\backslash det\; B$ 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 $\backslash det$ and the formula for the inverse involving the adjugate matrix mentioned below. In this event, the determinant of the inverse matrix is given by :$\backslash det\backslash left(A^\backslash right)\; =\; \backslash frac\; =;\; href="/html/ALL/s/det(A).html"\; ;"title="det(A)">det(A)$. 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 $\backslash operatorname\_n$ (respectively, asubgroup
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called the special linear group $\backslash operatorname\_n\; \backslash subset\; \backslash operatorname\_n$. 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 $(n-1)\; \backslash times\; (n-1)$-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 :$\backslash det(A)\; =\; \backslash sum\_^n\; (-1)^\; a\_\; M\_,$ 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: :$\backslash begina\&b\&c\backslash \backslash \; d\&e\&f\backslash \backslash \; g\&h\&i\backslash end\; =\; a\backslash begine\&f\backslash \backslash \; h\&i\backslash end\; -\; b\backslash begind\&f\backslash \backslash \; g\&i\backslash end\; +\; c\backslash begind\&e\backslash \backslash \; g\&h\backslash end$ Unwinding the determinants of these $2\; \backslash times\; 2$-matrices gives back the Leibniz formula mentioned above. Similarly, the ''Laplace expansion along the $j$-th column'' is the equality :$\backslash det(A)=\; \backslash sum\_^n\; (-1)^\; a\_\; M\_.$ 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 $$\backslash begin\; 1\; \&\; 1\; \&\; 1\; \&\; \backslash cdots\; \&\; 1\; \backslash \backslash \; x\_1\; \&\; x\_2\; \&\; x\_3\; \&\; \backslash cdots\; \&\; x\_n\; \backslash \backslash \; x\_1^2\; \&\; x\_2^2\; \&\; x\_3^2\; \&\; \backslash cdots\; \&\; x\_n^2\; \backslash \backslash \; \backslash vdots\; \&\; \backslash vdots\; \&\; \backslash vdots\; \&\; \backslash ddots\; \&\; \backslash vdots\; \backslash \backslash \; x\_1^\; \&\; x\_2^\; \&\; x\_3^\; \&\; \backslash cdots\; \&\; x\_n^\; \backslash end\; =\; \backslash prod\_\; \backslash left(x\_j\; -\; x\_i\backslash right).$$ This determinant has been applied, for example, in the proof of Baker's theorem in the theory oftranscendental number
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s.
Adjugate matrix

The adjugate matrix $\backslash operatorname(A)$ is the transpose of the matrix of the cofactors, that is, : $(\backslash operatorname(A))\_\; =\; (-1)^\; M\_.$ For every matrix, one has : $(\backslash det\; A)\; I\; =\; A\backslash operatornameA\; =\; (\backslash operatornameA)\backslash ,A.$ Thus the adjugate matrix can be used for expressing the inverse of a nonsingular matrix: : $A^\; =\; \backslash frac\; 1\backslash operatornameA.$Block matrices

The formula for the determinant of a $2\; \backslash times\; 2$-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 $n\; \backslash times\; n$, $n\; \backslash times\; m$, $m\; \backslash times\; n$ and $m\; \backslash times\; m$, respectively. The easiest such formula, which can be proven using either the Leibniz formula or a factorization involving the Schur complement, is :$\backslash det\backslash beginA\&\; 0\backslash \backslash \; C\&\; D\backslash end\; =\; \backslash det(A)\; \backslash det(D)\; =\; \backslash det\backslash beginA\&\; B\backslash \backslash \; 0\&\; D\backslash end.$ If $A$ is invertible (and similarly if $D$ is invertible), one has :$\backslash det\backslash beginA\&\; B\backslash \backslash \; C\&\; D\backslash end\; =\; \backslash det(A)\; \backslash det\backslash left(D\; -\; C\; A^\; B\backslash right)\; .$ If $D$ is a $1\; \backslash times\; 1$-matrix, this simplifies to $\backslash det\; (A)\; (D\; -\; CA^B)$. 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 :$\backslash det\backslash beginA\&\; B\backslash \backslash \; C\&\; D\backslash end\; =\; \backslash det(AD\; -\; BC).$ This formula has been generalized to matrices composed of more than $2\; \backslash times\; 2$ 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) :$\backslash det\backslash beginA\&\; B\backslash \backslash \; B\&\; A\backslash end\; =\; \backslash det(A\; -\; B)\; \backslash det(A\; +\; B).$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): :$\backslash det\backslash left(I\_\backslash mathit\; +\; AB\backslash right)\; =\; \backslash det\backslash left(I\_\backslash mathit\; +\; BA\backslash right),$ where ''I''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, $$\backslash det(A\; +\; B\; +\; C)\; +\; \backslash det(C)\; \backslash geq\; \backslash det(A\; +\; C)\; +\; \backslash det(B\; +\; C)\backslash text$$ with the corollary $$\backslash det(A\; +\; B)\; \backslash geq\; \backslash det(A)\; +\; \backslash det(B)\backslash text$$ Conversely, if $A$ and $B$ are Hermitian, positive-definite, and size $n\backslash times\; n$, then the determinant has concave $n$Sum identity for 2×2 matrices

For the special case of $2\backslash times\; 2$ matrices with complex entries, the determinant of the sum can be written in terms of determinants and traces in the following identity: :$\backslash det(A+B)\; =\; \backslash det(A)\; +\; \backslash det(B)\; +\; \backslash text(A)\backslash text(B)\; -\; \backslash text(AB).$ This has an application to $2\backslash times\; 2$ matrix algebras. For example, consider the complex numbers as a matrix algebra. The complex numbers have a representation as matrices of the form $$aI\; +\; b\backslash mathbf\; :=\; a\backslash begin\; 1\; \&\; 0\; \backslash \backslash \; 0\; \&\; 1\; \backslash end\; +\; b\backslash begin\; 0\; \&\; -1\; \backslash \backslash \; 1\; \&\; 0\; \backslash end$$ with $a$ and $b$ real. Since $\backslash text(\backslash mathbf)\; =\; 0$, taking $A\; =\; aI$ and $B\; =\; b\backslash mathbf$ in the above identity gives :$\backslash det(aI\; +\; b\backslash mathbf)\; =\; a^2\backslash det(I)\; +\; b^2\backslash det(\backslash mathbf)\; =\; a^2\; +\; b^2.$ This result followed just from $\backslash text(\backslash mathbf)\; =\; 0$ and $\backslash det(I)\; =\; \backslash det(\backslash mathbf)\; =\; 1$.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, theeigenvalue
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s and the characteristic polynomial of a matrix. Let $A$ be an $n\; \backslash times\; n$-matrix with complex entries with eigenvalues
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$\backslash lambda\_1,\; \backslash lambda\_2,\; \backslash ldots,\; \backslash lambda\_n$. (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,
:$\backslash det(A)\; =\; \backslash prod\_^n\; \backslash lambda\_i=\backslash lambda\_1\backslash lambda\_2\backslash cdots\backslash lambda\_n.$
The product of all non-zero eigenvalues is referred to as pseudo-determinant.
The characteristic polynomial is defined as
:$\backslash chi\_A(t)\; =\; \backslash det(t\; \backslash cdot\; I\; -\; A).$
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 eigenvalue
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s of the matrix $A$: they are precisely the roots of this polynomial, i.e., those complex numbers $\backslash lambda$ such that
:$\backslash chi\_A(\backslash lambda)\; =\; 0.$
A Hermitian matrix
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is positive definite if all its eigenvalues are positive. Sylvester's criterion asserts that this is equivalent to the determinants of the submatrices
:$A\_k\; :=\; \backslash begin\; a\_\; \&\; a\_\; \&\; \backslash cdots\; \&\; a\_\; \backslash \backslash \; a\_\; \&\; a\_\; \&\; \backslash cdots\; \&\; a\_\; \backslash \backslash \; \backslash vdots\; \&\; \backslash vdots\; \&\; \backslash ddots\; \&\; \backslash vdots\; \backslash \backslash \; a\_\; \&\; a\_\; \&\; \backslash cdots\; \&\; a\_\; \backslash end$
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 , :$\backslash det(\backslash exp(A))\; =\; \backslash exp(\backslash operatorname(A))$ or, for real matrices , :$\backslash operatorname(A)\; =\; \backslash log(\backslash det(\backslash exp(A))).$ 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 :$\backslash exp(L)\; =\; A$ the determinant of is given by :$\backslash det(A)\; =\; \backslash exp(\backslash operatorname(L)).$ For example, for , , and , respectively, :$\backslash begin\; \backslash det(A)\; \&=\; \backslash frac\backslash left(\backslash left(\backslash operatorname(A)\backslash right)^2\; -\; \backslash operatorname\backslash left(A^2\backslash right)\backslash right),\; \backslash \backslash \; \backslash det(A)\; \&=\; \backslash frac\backslash left(\backslash left(\backslash operatorname(A)\backslash right)^3\; -\; 3\backslash operatorname(A)\; ~\; \backslash operatorname\backslash left(A^2\backslash right)\; +\; 2\; \backslash operatorname\backslash left(A^3\backslash right)\backslash right),\; \backslash \backslash \; \backslash det(A)\; \&=\; \backslash frac\backslash left(\backslash left(\backslash operatorname(A)\backslash right)^4\; -\; 6\backslash operatorname\backslash left(A^2\backslash right)\backslash left(\backslash operatorname(A)\backslash right)^2\; +\; 3\backslash left(\backslash operatorname\backslash left(A^2\backslash right)\backslash right)^2\; +\; 8\backslash operatorname\backslash left(A^3\backslash right)~\backslash operatorname(A)\; -\; 6\backslash operatorname\backslash left(A^4\backslash right)\backslash right).\; \backslash end$ cf. Cayley-Hamilton theorem. Such expressions are deducible from combinatorial arguments,Newton's identities
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, or the Faddeev–LeVerrier algorithm. That is, for generic , the signed constant term of the characteristic polynomial, determined recursively from
:$c\_n\; =\; 1;\; ~~~c\_\; =\; -\backslash frac\backslash sum\_^m\; c\_\; \backslash operatorname\backslash left(A^k\backslash right)\; ~~(1\; \backslash le\; m\; \backslash le\; n)~.$
In the general case, this may also be obtained from
:$\backslash det(A)\; =\; \backslash sum\_\backslash prod\_^n\; \backslash frac\; \backslash operatorname\backslash left(A^l\backslash right)^,$
where the sum is taken over the set of all integers satisfying the equation
:$\backslash sum\_^n\; lk\_l\; =\; n.$
The formula can be expressed in terms of the complete exponential Bell polynomial of ''n'' arguments ''s''Upper and lower bounds

For a positive definite matrix , the trace operator gives the following tight lower and upper bounds on the log determinant :$\backslash operatorname\backslash left(I\; -\; A^\backslash right)\; \backslash le\; \backslash log\backslash det(A)\; \backslash le\; \backslash operatorname(A\; -\; I)$ 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, :$\backslash frac\; \backslash leq\; \backslash det(A)^\backslash frac\; \backslash leq\; \backslash frac\backslash operatorname(A)\; \backslash leq\; \backslash sqrt.$ 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 thegeometric mean
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, which is less than the arithmetic mean
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, which is, in turn, less than the root mean square
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.
Derivative

The Leibniz formula shows that the determinant of real (or analogously for complex) square matrices is apolynomial
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function from $\backslash mathbf\; R^$ to $\backslash mathbf\; R$. In particular, it is everywhere differentiable. Its derivative can be expressed using Jacobi's formula:
:$\backslash frac\; =\; \backslash operatorname\backslash left(\backslash operatorname(A)\; \backslash frac\backslash right).$
where $\backslash operatorname(A)$ denotes the adjugate of $A$. In particular, if $A$ is invertible, we have
:$\backslash frac\; =\; \backslash det(A)\; \backslash operatorname\backslash left(A^\; \backslash frac\backslash right).$
Expressed in terms of the entries of $A$, these are
: $\backslash frac=\; \backslash operatorname(A)\_\; =\; \backslash det(A)\backslash left(A^\backslash right)\_.$
Yet another equivalent formulation is
:$\backslash det(A\; +\; \backslash epsilon\; X)\; -\; \backslash det(A)\; =\; \backslash operatorname(\backslash operatorname(A)\; X)\; \backslash epsilon\; +\; O\backslash left(\backslash epsilon^2\backslash right)\; =\; \backslash det(A)\; \backslash operatorname\backslash left(A^\; X\backslash right)\; \backslash epsilon\; +\; O\backslash left(\backslash epsilon^2\backslash right)$,
using big O notation
Big ''O'' notation is a mathematical notation that describes the asymptotic analysis, limiting behavior of a function (mathematics), function when the Argument of a function, argument tends towards a particular value or infinity. Big O is a memb ...

. The special case where $A\; =\; I$, the identity matrix, yields
:$\backslash det(I\; +\; \backslash epsilon\; X)\; =\; 1\; +\; \backslash operatorname(X)\; \backslash epsilon\; +\; O\backslash left(\backslash epsilon^2\backslash right).$
This identity is used in describing Lie algebras associated to certain matrix Lie groups. For example, the special linear group $\backslash operatorname\_n$ is defined by the equation $\backslash det\; A\; =\; 1$. The above formula shows that its Lie algebra is the special linear Lie algebra $\backslash mathfrak\_n$ consisting of those matrices having trace zero.
Writing a $3\; \backslash times\; 3$-matrix as $A\; =\; \backslash begina\; \&\; b\; \&\; c\backslash end$ 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:
: $\backslash begin\; \backslash nabla\_\backslash mathbf\backslash det(A)\; \&=\; \backslash mathbf\; \backslash times\; \backslash mathbf\; \backslash \backslash \; \backslash nabla\_\backslash mathbf\backslash det(A)\; \&=\; \backslash mathbf\; \backslash times\; \backslash mathbf\; \backslash \backslash \; \backslash nabla\_\backslash mathbf\backslash det(A)\; \&=\; \backslash mathbf\; \backslash times\; \backslash mathbf.\; \backslash end$
History

Historically, determinants were used long before matrices: A determinant was originally defined as a property of asystem of linear equations
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.
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 curve
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s 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 (1773) treated determinants of the second and third order and applied it to questions of elimination theory; he proved many special cases of general identities.
Gauss (1801) made the next advance. Like Lagrange, he made much use of determinants in the theory of numbers. He introduced the word "determinant" (Laplace had used "resultant"), though not in the present signification, but rather as applied to the discriminant of a quantic. Gauss also arrived at the notion of reciprocal (inverse) determinants, and came very near the multiplication theorem.
The next contributor of importance is Binet (1811, 1812), who formally stated the theorem relating to the product of two matrices of ''m'' columns and ''n'' rows, which for the special case of reduces to the multiplication theorem. On the same day (November 30, 1812) that Binet presented his paper to the Academy, Cauchy also presented one on the subject. (See Cauchy–Binet formula.) In this he used the word "determinant" in its present sense, summarized and simplified what was then known on the subject, improved the notation, and gave the multiplication theorem with a proof more satisfactory than Binet's. With him begins the theory in its generality.
used the functional determinant which Sylvester later called the Jacobian. In his memoirs in '' Crelle's Journal'' for 1841 he specially treats this subject, as well as the class of alternating functions which Sylvester has called ''alternants''. About the time of Jacobi's last memoirs, Sylvester (1839) and Cayley began their work. introduced the modern notation for the determinant using vertical bars.
The study of special forms of determinants has been the natural result of the completion of the general theory. Axisymmetric determinants have been studied by Lebesgue, Hesse
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, and Sylvester; persymmetric determinants by Sylvester and Hankel; circulants by Catalan, Spottiswoode, Glaisher, and Scott; skew determinants and Pfaffians, in connection with the theory of orthogonal transformation, by Cayley; continuants by Sylvester; Wronskians (so called by Muir
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Places United States
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) by Christoffel and Frobenius; compound determinants by Sylvester, Reiss, and Picquet; Jacobians and Hessians by Sylvester; and symmetric gauche determinants by Trudi. Of the textbooks on the subject Spottiswoode's was the first. In America, Hanus (1886), Weld (1893), and Muir/Metzler (1933) published treatises.
Applications

Cramer's rule

Determinants can be used to describe the solutions of a linear system of equations, written in matrix form as $Ax\; =\; b$. This equation has a unique solution $x$ if and only if $\backslash det\; (A)$ is nonzero. In this case, the solution is given by Cramer's rule: :$x\_i\; =\; \backslash frac\; \backslash qquad\; i\; =\; 1,\; 2,\; 3,\; \backslash ldots,\; n$ where $A\_i$ is the matrix formed by replacing the $i$-th column of $A$ by the column vector $b$. This follows immediately by column expansion of the determinant, i.e. :$\backslash det(A\_i)\; =\; \backslash det\backslash begina\_1\; \&\; \backslash ldots\; \&\; b\; \&\; \backslash ldots\; \&\; a\_n\backslash end\; =\; \backslash sum\_^n\; x\_j\backslash det\backslash begina\_1\; \&\; \backslash ldots\; \&\; a\_\; \&\; a\_j\; \&\; a\_\; \&\; \backslash ldots\; \&\; a\_n\backslash end\; =\; x\_i\backslash det(A)$ where the vectors $a\_j$ are the columns of ''A''. The rule is also implied by the identity :$A\backslash ,\; \backslash operatorname(A)\; =\; \backslash operatorname(A)\backslash ,\; A\; =\; \backslash det(A)\backslash ,\; I\_n.$ Cramer's rule can be implemented in $\backslash operatorname\; O(n^3)$ time, which is comparable to more common methods of solving systems of linear equations, such as LU, QR, or singular value decomposition.Linear independence

Determinants can be used to characterize linearly dependent vectors: $\backslash det\; A$ is zero if and only if the column vectors (or, equivalently, the row vectors) of the matrix $A$ are linearly dependent. For example, given two linearly independent vectors $v\_1,\; v\_2\; \backslash in\; \backslash mathbf\; R^3$, a third vector $v\_3$ lies in the plane spanned by the former two vectors exactly if the determinant of the $3\; \backslash times\; 3$-matrix consisting of the three vectors is zero. The same idea is also used in the theory ofdifferential equation
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s: given functions $f\_1(x),\; \backslash dots,\; f\_n(x)$ (supposed to be $n-1$ times differentiable), the Wronskian is defined to be
:$W(f\_1,\; \backslash ldots,\; f\_n)(x)\; =\; \backslash begin\; f\_1(x)\; \&\; f\_2(x)\; \&\; \backslash cdots\; \&\; f\_n(x)\; \backslash \backslash \; f\_1\text{'}(x)\; \&\; f\_2\text{'}(x)\; \&\; \backslash cdots\; \&\; f\_n\text{'}(x)\; \backslash \backslash \; \backslash vdots\; \&\; \backslash vdots\; \&\; \backslash ddots\; \&\; \backslash vdots\; \backslash \backslash \; f\_1^(x)\; \&\; f\_2^(x)\; \&\; \backslash cdots\; \&\; f\_n^(x)\; \backslash end.$
It is non-zero (for some $x$) in a specified interval if and only if the given functions and all their derivatives up to order $n-1$ are linearly independent. If it can be shown that the Wronskian is zero everywhere on an interval then, in the case of analytic functions, this implies the given functions are linearly dependent. See the Wronskian and linear independence. Another such use of the determinant is the resultant, which gives a criterion when two polynomial
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s have a common root
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.
Orientation of a basis

The determinant can be thought of as assigning a number to everysequence
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of ''n'' vectors in REuclidean space
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. The determinant of such a matrix determines whether the orientation of the basis is consistent with or opposite to the orientation of the standard basis
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. If the determinant is +1, the basis has the same orientation. If it is −1, the basis has the opposite orientation.
More generally, if the determinant of ''A'' is positive, ''A'' represents an orientation-preserving linear transformation (if ''A'' is an orthogonal or matrix, this is a rotation
Rotation, or spin, is the circular movement of an object around a '' central axis''. A two-dimensional rotating object has only one possible central axis and can rotate in either a clockwise or counterclockwise direction. A three-dimensional ...

), while if it is negative, ''A'' switches the orientation of the basis.
Volume and Jacobian determinant

As pointed out above, the absolute value of the determinant of real vectors is equal to the volume of theparallelepiped
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spanned by those vectors. As a consequence, if $f\; :\; \backslash mathbf\; R^n\; \backslash to\; \backslash mathbf\; R^n$ is the linear map given by multiplication with a matrix $A$, and $S\; \backslash subset\; \backslash mathbf\; R^n$ is any measurable subset
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, then the volume of $f(S)$ is given by $,\; \backslash det(A),$ times the volume of $S$. More generally, if the linear map $f\; :\; \backslash mathbf\; R^n\; \backslash to\; \backslash mathbf\; R^m$ is represented by the $m\; \backslash times\; n$-matrix $A$, then the $n$- dimensional volume of $f(S)$ is given by:
:$\backslash operatorname(f(S))\; =\; \backslash sqrt\; \backslash operatorname(S).$
By calculating the volume of the tetrahedron
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bounded by four points, they can be used to identify skew lines. The volume of any tetrahedron, given its vertices $a,\; b,\; c,\; d$, $\backslash frac\; 1\; 6\; \backslash cdot\; ,\; \backslash det(a-b,b-c,c-d),$, or any other combination of pairs of vertices that form a spanning tree over the vertices.
For a general differentiable function, much of the above carries over by considering the Jacobian matrix of ''f''. For
:$f:\; \backslash mathbf\; R^n\; \backslash rightarrow\; \backslash mathbf\; R^n,$
the Jacobian matrix is the matrix whose entries are given by the partial derivatives
:$D(f)\; =\; \backslash left(\backslash frac\; \backslash right)\_.$
Its determinant, the Jacobian determinant, appears in the higher-dimensional version of integration by substitution
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: for suitable functions ''f'' and an open subset ''U'' of RCartography
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, the determinant can be used to measure the rate of expansion of a map near the poles.
Abstract algebraic aspects

Determinant of an endomorphism

The above identities concerning the determinant of products and inverses of matrices imply that similar matrices have the same determinant: two matrices ''A'' and ''B'' are similar, if there exists an invertible matrix ''X'' such that . Indeed, repeatedly applying the above identities yields :$\backslash det(A)\; =\; \backslash det(X)^\; \backslash det(B)\backslash det(X)\; =\; \backslash det(B)\; \backslash det(X)^\; \backslash det(X)\; =\; \backslash det(B).$ The determinant is therefore also called a similarity invariant. The determinant of a linear transformation :$T\; :\; V\; \backslash to\; V$ for some finite-dimensionalvector space
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''V'' is defined to be the determinant of the matrix describing it, with respect to an arbitrary choice of basis in ''V''. By the similarity invariance, this determinant is independent of the choice of the basis for ''V'' and therefore only depends on the endomorphism ''T''.
Square matrices over commutative rings

The above definition of the determinant using the Leibniz rule holds works more generally when the entries of the matrix are elements of a commutative ring $R$, such as the integers $\backslash mathbf\; Z$, as opposed to the field of real or complex numbers. Moreover, the characterization of the determinant as the unique alternating multilinear map that satisfies $\backslash det(I)\; =\; 1$ still holds, as do all the properties that result from that characterization. A matrix $A\; \backslash in\; \backslash operatorname\_(R)$ is invertible (in the sense that there is an inverse matrix whose entries are in $R$) if and only if its determinant is an invertible element in $R$. For $R\; =\; \backslash mathbf\; Z$, this means that the determinant is +1 or −1. Such a matrix is called unimodular. The determinant being multiplicative, it defines a group homomorphism :$\backslash operatorname\_n(R)\; \backslash rightarrow\; R^\backslash times,$ between the general linear group (the group of invertible $n\; \backslash times\; n$-matrices with entries in $R$) and the multiplicative group of units in $R$. Since it respects the multiplication in both groups, this map is a group homomorphism. Given a ring homomorphism $f\; :\; R\; \backslash to\; S$, there is a map $\backslash operatorname\_n(f)\; :\; \backslash operatorname\_n(R)\; \backslash to\; \backslash operatorname\_n(S)$ given by replacing all entries in $R$ by their images under $f$. The determinant respects these maps, i.e., the identity :$f(\backslash det((a\_)))\; =\; \backslash det\; ((f(a\_)))$ holds. In other words, the displayed commutative diagram commutes. For example, the determinant of the complex conjugate of a complex matrix (which is also the determinant of its conjugate transpose) is the complex conjugate of its determinant, and for integer matrices: the reduction modulo $m$ of the determinant of such a matrix is equal to the determinant of the matrix reduced modulo $m$ (the latter determinant being computed usingmodular arithmetic
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). In the language of category theory, the determinant is a natural transformation between the two functors $\backslash operatorname\_n$ and $(-)^\backslash times$. Adding yet another layer of abstraction, this is captured by saying that the determinant is a morphism of algebraic groups, from the general linear group to the multiplicative group,
:$\backslash det:\; \backslash operatorname\_n\; \backslash to\; \backslash mathbb\; G\_m.$
Exterior algebra

The determinant of a linear transformation $T\; :\; V\; \backslash to\; V$ of an $n$-dimensional vector space $V$ or, more generally a free module of (finite)rank
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$n$ over a commutative ring $R$ can be formulated in a coordinate-free manner by considering the $n$-th exterior power $\backslash bigwedge^n\; V$ of $V$. The map $T$ induces a linear map
:$\backslash begin\; \backslash bigwedge^n\; T:\; \backslash bigwedge^n\; V\; \&\backslash rightarrow\; \backslash bigwedge^n\; V\; \backslash \backslash \; v\_1\; \backslash wedge\; v\_2\; \backslash wedge\; \backslash dots\; \backslash wedge\; v\_n\; \&\backslash mapsto\; T\; v\_1\; \backslash wedge\; T\; v\_2\; \backslash wedge\; \backslash dots\; \backslash wedge\; T\; v\_n.\; \backslash end$
As $\backslash bigwedge^n\; V$ is one-dimensional, the map $\backslash bigwedge^n\; T$ is given by multiplying with some scalar, i.e., an element in $R$. Some authors such as use this fact to ''define'' the determinant to be the element in $R$ satisfying the following identity (for all $v\_i\; \backslash in\; V$):
:$\backslash left(\backslash bigwedge^n\; T\backslash right)\backslash left(v\_1\; \backslash wedge\; \backslash dots\; \backslash wedge\; v\_n\backslash right)\; =\; \backslash det(T)\; \backslash cdot\; v\_1\; \backslash wedge\; \backslash dots\; \backslash wedge\; v\_n.$
This definition agrees with the more concrete coordinate-dependent definition. This can be shown using the uniqueness of a multilinear alternating form on $n$-tuples of vectors in $R^n$.
For this reason, the highest non-zero exterior power $\backslash bigwedge^n\; V$ (as opposed to the determinant associated to an endomorphism) is sometimes also called the determinant of $V$ and similarly for more involved objects such as vector bundles or chain complexes of vector spaces. Minors of a matrix can also be cast in this setting, by considering lower alternating forms $\backslash bigwedge^k\; V$ with $k\; <\; n$.
Generalizations and related notions

Determinants as treated above admit several variants: the permanent of a matrix is defined as the determinant, except that the factors $\backslash sgn(\backslash sigma)$ occurring in Leibniz's rule are omitted. The immanant generalizes both by introducing a character of thesymmetric group
In abstract algebra, the symmetric group defined over any set is the group whose elements are all the bijection
In mathematics, a bijection, also known as a bijective function, one-to-one correspondence, or invertible function, is a ...

$S\_n$ in Leibniz's rule.
Determinants for finite-dimensional algebras

For any associative algebra $A$ that is finite-dimensional as a vector space over a field $F$, there is a determinant map :$\backslash det\; :\; A\; \backslash to\; F.$ This definition proceeds by establishing the characteristic polynomial independently of the determinant, and defining the determinant as the lowest order term of this polynomial. This general definition recovers the determinant for the matrix algebra $A\; =\; \backslash operatorname\_(F)$, but also includes several further cases including the determinant of a quaternion, :$\backslash det\; (a\; +\; ib+jc+kd)\; =\; a^2\; +\; b^2\; +\; c^2\; +\; d^2$, the norm $N\_\; :\; L\; \backslash to\; F$ of afield extension
In mathematics
Mathematics is an area of knowledge that includes the topics of numbers, formulas and related structures, shapes and the spaces in which they are contained, and quantities and their changes. These topics are represented in mo ...

, as well as the Pfaffian of a skew-symmetric matrix and the reduced norm of a central simple algebra, also arise as special cases of this construction.
Infinite matrices

For matrices with an infinite number of rows and columns, the above definitions of the determinant do not carry over directly. For example, in the Leibniz formula, an infinite sum (all of whose terms are infinite products) would have to be calculated. Functional analysis provides different extensions of the determinant for such infinite-dimensional situations, which however only work for particular kinds of operators. The Fredholm determinant defines the determinant for operators known as trace class operators by an appropriate generalization of the formula :$\backslash det(I+A)\; =\; \backslash exp(\backslash operatorname(\backslash log(I+A))).$ Another infinite-dimensional notion of determinant is the functional determinant.Operators in von Neumann algebras

For operators in a finite factor, one may define a positive real-valued determinant called the Fuglede−Kadison determinant using the canonical trace. In fact, corresponding to every tracial state on a von Neumann algebra there is a notion of Fuglede−Kadison determinant.Related notions for non-commutative rings

For matrices over non-commutative rings, multilinearity and alternating properties are incompatible for , so there is no good definition of the determinant in this setting. For square matrices with entries in a non-commutative ring, there are various difficulties in defining determinants analogously to that for commutative rings. A meaning can be given to the Leibniz formula provided that the order for the product is specified, and similarly for other definitions of the determinant, but non-commutativity then leads to the loss of many fundamental properties of the determinant, such as the multiplicative property or that the determinant is unchanged under transposition of the matrix. Over non-commutative rings, there is no reasonable notion of a multilinear form (existence of a nonzero with a regular element of ''R'' as value on some pair of arguments implies that ''R'' is commutative). Nevertheless, various notions of non-commutative determinant have been formulated that preserve some of the properties of determinants, notably quasideterminants and the Dieudonné determinant. For some classes of matrices with non-commutative elements, one can define the determinant and prove linear algebra theorems that are very similar to their commutative analogs. Examples include the ''q''-determinant on quantum groups, the Capelli determinant on Capelli matrices, and the Berezinian on supermatrices (i.e., matrices whose entries are elements of $\backslash mathbb\; Z\_2$- graded rings). Manin matrices form the class closest to matrices with commutative elements.Calculation

Determinants are mainly used as a theoretical tool. They are rarely calculated explicitly in numerical linear algebra, where for applications like checking invertibility and finding eigenvalues the determinant has largely been supplanted by other techniques.Computational geometry
Computational geometry is a branch of computer science
Computer science is the study of computation, automation, and information. Computer science spans theoretical disciplines (such as algorithms, theory of computation, information ...

, however, does frequently use calculations related to determinants.
While the determinant can be computed directly using the Leibniz rule this approach is extremely inefficient for large matrices, since that formula requires calculating $n!$ ($n$ factorial
In mathematics
Mathematics is an area of knowledge that includes the topics of numbers, formulas and related structures, shapes and the spaces in which they are contained, and quantities and their changes. These topics are represented i ...

) products for an $n\; \backslash times\; n$-matrix. Thus, the number of required operations grows very quickly: it is of order $n!$. The Laplace expansion is similarly inefficient. Therefore, more involved techniques have been developed for calculating determinants.
Decomposition methods

Some methods compute $\backslash det(A)$ by writing the matrix as a product of matrices whose determinants can be more easily computed. Such techniques are referred to as decomposition methods. Examples include the LU decomposition, the QR decomposition or the Cholesky decomposition (for positive definite matrices). These methods are of order $\backslash operatorname\; O(n^3)$, which is a significant improvement over $\backslash operatorname\; O\; (n!)$. For example, LU decomposition expresses $A$ as a product :$A\; =\; PLU.$ of a permutation matrix $P$ (which has exactly a single $1$ in each column, and otherwise zeros), a lower triangular matrix $L$ and an upper triangular matrix $U$. The determinants of the two triangular matrices $L$ and $U$ can be quickly calculated, since they are the products of the respective diagonal entries. The determinant of $P$ is just the sign $\backslash varepsilon$ of the corresponding permutation (which is $+1$ for an even number of permutations and is $-1$ for an odd number of permutations). Once such a LU decomposition is known for $A$, its determinant is readily computed as :$\backslash det(A)\; =\; \backslash varepsilon\; \backslash det(L)\backslash cdot\backslash det(U).$Further methods

The order $\backslash operatorname\; O(n^3)$ reached by decomposition methods has been improved by different methods. If two matrices of order $n$ can be multiplied in time $M(n)$, where $M(n)\; \backslash ge\; n^a$ for some $a>2$, then there is an algorithm computing the determinant in time $O(M(n))$. This means, for example, that an $\backslash operatorname\; O(n^)$ algorithm for computing the determinant exists based on the Coppersmith–Winograd algorithm. This exponent has been further lowered, as of 2016, to 2.373. In addition to the complexity of the algorithm, further criteria can be used to compare algorithms. Especially for applications concerning matrices over rings, algorithms that compute the determinant without any divisions exist. (By contrast, Gauss elimination requires divisions.) One such algorithm, having complexity $\backslash operatorname\; O(n^4)$ is based on the following idea: one replaces permutations (as in the Leibniz rule) by so-called closed ordered walks, in which several items can be repeated. The resulting sum has more terms than in the Leibniz rule, but in the process several of these products can be reused, making it more efficient than naively computing with the Leibniz rule. Algorithms can also be assessed according to their bit complexity, i.e., how many bits of accuracy are needed to store intermediate values occurring in the computation. For example, theGaussian elimination
In mathematics, Gaussian elimination, also known as row reduction, is an algorithm
In mathematics
Mathematics is an area of knowledge that includes the topics of numbers, formulas and related structures, shapes and the spaces in w ...

(or LU decomposition) method is of order $\backslash operatorname\; O(n^3)$, but the bit length of intermediate values can become exponentially long. By comparison, the Bareiss Algorithm, is an exact-division method (so it does use division, but only in cases where these divisions can be performed without remainder) is of the same order, but the bit complexity is roughly the bit size of the original entries in the matrix times $n$.
If the determinant of ''A'' and the inverse of ''A'' have already been computed, the matrix determinant lemma allows rapid calculation of the determinant of , where ''u'' and ''v'' are column vectors.
Charles Dodgson (i.e. Lewis Carroll
Charles Lutwidge Dodgson (; 27 January 1832 – 14 January 1898), better known by his pen name Lewis Carroll, was an English author, poet and mathematician. His most notable works are ''Alice's Adventures in Wonderland'' (1865) and its sequel ...

of '' Alice's Adventures in Wonderland'' fame) invented a method for computing determinants called Dodgson condensation. Unfortunately this interesting method does not always work in its original form.
See also

* Cauchy determinant * Cayley–Menger determinant * Dieudonné determinant * Slater determinant * Determinantal conjectureNotes

References

* * * * * * * * * * * * * * * * * * * * G. Baley Price (1947) "Some identities in the theory of determinants",American Mathematical Monthly
''The American Mathematical Monthly'' is a mathematical journal founded by Benjamin Finkel in 1894. It is published ten times each year by Taylor & Francis
Taylor & Francis Group is an international company originating in England that pu ...

54:75–90
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Historical references

* * * * * * * * *External links

* * *Determinant Interactive Program and Tutorial

Linear algebra: determinants.

Compute determinants of matrices up to order 6 using Laplace expansion you choose.

Determinant Calculator

Calculator for matrix determinants, up to the 8th order.

Determinants explained in an easy fashion in the 4th chapter as a part of a Linear Algebra course.

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