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Det [m]

gives the determinant of the square matrix m.

Details and Options
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Basic Uses  
Special Matrices  
Options  
Modulus  
Applications  
Area and Volumes  
Orientation and Rotations  
Linear and Abstract Algebra  
Properties & Relations  
Neat Examples  
See Also
Tech Notes
Related Guides
Related Links
History
Cite this Page

Det

Det [m]

gives the determinant of the square matrix m.

Details and Options

  • Det [m,Modulus->n] computes the determinant modulo n.

Examples

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Basic Examples  (2)

Find the determinant of a symbolic matrix:

The determinant of an exact matrix:

Scope  (13)

Basic Uses  (8)

Find the determinant of a MachinePrecision matrix:

Determinant of a complex matrix:

Determinant of an exact matrix:

Determinant of an arbitrary-precision matrix:

Determinant of a symbolic matrix:

The determinant of a large numerical matrix is computed efficiently:

Note that the result may not be a machine number:

Determinant of a matrix with finite field elements:

Determinant of a CenteredInterval matrix:

Find a random representative mrep of m:

Verify that mdet contains the determinant of mrep:

Special Matrices  (5)

Determinants of sparse matrices:

Determinants of structured matrices:

IdentityMatrix always has unit determinant:

Determinant of HilbertMatrix :

Compute the determinant of a matrix of univariate polynomials of degree :

Options  (1)

Modulus  (1)

Compute a determinant using arithmetic modulo 47:

This is faster than computing Mod [Det[m],47]:

Applications  (19)

Area and Volumes  (6)

Use Det to find area of a parallelogram spanned by and :

Visualize the parallelogram when one vertex is at the origin:

The area is given by the absolute value of the determinant:

Compare with the result given by Area :

Use Det to find the volume of a parallelepiped spanned by , and :

Visualize the parallelepiped when one vertex is at the origin:

The volume is given by the absolute value of the determinant:

Compare with a direct computation using Volume :

Use Det to find hypervolume of a hyper-parallelepiped spanned by the following vectors:

The hypervolume is given by the absolute value of the determinant:

Compare with the result given by RegionMeasure :

The determinant itself is negative, so the are not right-handed:

Simply reorder any two vectors, say the middle two, to produce a right-handed set:

Find the area of the image of the unit disk under the linear transformation associated to the matrix :

The area of the image is given by sqrt(TemplateBox[{{TemplateBox[{m}, Transpose, SyntaxForm -> SuperscriptBox], ., m}}, Det]) Area[D]=pi sqrt(TemplateBox[{{TemplateBox[{m}, Transpose, SyntaxForm -> SuperscriptBox], ., m}}, Det]):

Compare with a direct computation:

Visualize the image :

Find the volume factor in the change of variables formula between Cartesian and polar coordinates. The mapping from polar to Cartesian coordinates is given by:

Compute the Jacobian of the mapping using Grad :

By the change of variables theorem, the volume is the determinant of the Jacobian:

Compare with the result given by CoordinateChartData :

The same procedure will work with any coordinate system, for example, spherical coordinates:

Use the change of variables theorem to compute , where is the following region:

First, define hyperbolic coordinates as follows:

The region clearly corresponds to and . By the change of variables formula, intint1dudv=intintTemplateBox[{TemplateBox[{{{, {u, ,, v}, }}, {{, {x, ,, y}, }}}, Grad, SyntaxForm -> Del]}, Det]dxdy. The gradient is given by:

The determinant of the gradient is twice the function whose integral is :

Hence, is given by the trivial integral :

Compare with a direct integration over the region:

Orientation and Rotations  (5)

Determine whether the following basis for TemplateBox[{}, Reals]^3 is right-handed:

The determinant of the matrix formed by the basis is negative, so it is not right-handed:

Determine if linear transformation corresponding to is orientation-preserving or orientation-reversing:

As TemplateBox[{m}, Det]>0, the mapping is orientation-preserving:

Show that the following matrix is not a rotation matrix:

All rotation matrices have unit determinant; since TemplateBox[{m}, Det]!=1, it cannot be a rotation matrix:

Show that the matrix is orthogonal and determine if it is a rotation matrix or includes a reflection:

Up to the input precision, TemplateBox[{m}, Transpose]=TemplateBox[{m}, Inverse], which shows that is orthogonal:

All orthogonal matrices have TemplateBox[{m}, Det]=+/-1, but rotations have TemplateBox[{m}, Det]=1; as TemplateBox[{m}, Det]=-1, includes a reflection:

The generalization of a rotation matrix to complex vector spaces is a special unitary matrix that is unitary and has unit determinant. Show that the following matrix is a special unitary matrix:

The matrix is unitary because TemplateBox[{u}, ConjugateTranspose]=TemplateBox[{u}, Inverse]:

It also has unit determinant, so it is in fact an element of the special unitary group :

Linear and Abstract Algebra  (8)

Determine the values of the parameter for which the system , has a unique solution and describe that solution. First, form the coefficient matrix and constant vector :

The solutions will be unique as TemplateBox[{a}, Det]!=0:

Solving over the reals gives three open intervals separated at and :

Since the matrix is invertible for these values of , the solution is simply TemplateBox[{a}, Inverse].b:

Verify the solution in the original system of equations:

Use Cramer's rule to solve the system of equations , , . First, form the coefficient matrix and constant vector :

Form the three matrices where replaces the corresponding columns of :

The entries of the solution are given by TemplateBox[{{d, _, j}}, Det]/TemplateBox[{a}, Det]:

Verify the result:

Write a function implementing Cramer's rule for solving a linear system m.x=b:

Use the function to solve a system for particular values of m and b:

Verify the solution:

For numerical systems, LinearSolve is much faster and more accurate:

Determine if the matrix has a nontrivial kernel (null space):

Since the determinant is nonzero, the kernel is trivial:

Confirm the result using NullSpace :

Determine if the mapping corresponding to the matrix is injective:

Since TemplateBox[{a}, Det]=0, the mapping is not injective:

Confirm the result using FunctionInjective :

Since defines a linear function f:TemplateBox[{}, Reals]^3->TemplateBox[{}, Reals]^3, the failure to be injective implies a failure to be surjective:

Determine if the matrix defines an automorphism (a bijective linear map):

Since TemplateBox[{a}, Det]!=0, the mapping is an automorphism:

Confirm the result using FunctionBijective :

Compute the cofactor obtained from removing row i and column j:

Check the result:

Modular computation of a determinant:

Modular determinants:

Recover the result:

Shift the residue to be symmetric:

Confirm that the non-modular determinant was recovered:

Properties & Relations  (14)

The determinant is the product of the eigenvalues:

Det satisfies TemplateBox[{a}, Det]=sum_sigma^(S_n)sgn[sigma]product_i^na〚i,sigma〚i〛〛, where is all -permutations and is Signature :

Det can be computed recursively via cofactor expansion along any row:

Or any column:

The determinant is the signed volume of the parallelepiped generated by its rows:

This equals the volume up to sign:

A square matrix has an inverse if and only if its determinant is nonzero:

The determinant of a triangular matrix is the product of its diagonal elements:

The determinant of a matrix product is the product of the determinants:

The determinant of the inverse is the reciprocal of the determinant:

A matrix and its transpose have equal determinants:

The determinant of the matrix exponential is the exponential of the trace:

CharacteristicPolynomial [m] is equal to :

Det [m] can be computed from LUDecomposition [m]:

Consider two rectangular matrices and such that and are both square:

Sylvester's determinant theorem states that TemplateBox[{{𝟙, +, {a, ., b}}}, Det]=TemplateBox[{{𝟙, +, {b, ., a}}}, Det], where is the matching identity matrix:

If a matrix is the TensorProduct of two vectors and , then TemplateBox[{{𝟙, +, m}}, Det]=1+u.v:

This can be expressed equally in terms of KroneckerProduct :

This follows from Sylvester's determinant theorem for the corresponding row and column matrices:

Neat Examples  (1)

Determinants of tridiagonal matrices:

A closed-form formula for these determinants is given by (a c)^(n/2) TemplateBox[{n, {b, /, {(, {2, , {sqrt(, {a, , c}, )}}, )}}}, ChebyshevU]:

History

Introduced in 1988 (1.0) | Updated in 2022 (13.2) 2024 (14.0)

Wolfram Research (1988), Det, Wolfram Language function, https://reference.wolfram.com/language/ref/Det.html (updated 2024).

Text

Wolfram Research (1988), Det, Wolfram Language function, https://reference.wolfram.com/language/ref/Det.html (updated 2024).

CMS

Wolfram Language. 1988. "Det." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2024. https://reference.wolfram.com/language/ref/Det.html.

APA

Wolfram Language. (1988). Det. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/Det.html

BibTeX

@misc{reference.wolfram_2025_det, author="Wolfram Research", title="{Det}", year="2024", howpublished="\url{https://reference.wolfram.com/language/ref/Det.html}", note=[Accessed: 16-November-2025]}

BibLaTeX

@online{reference.wolfram_2025_det, organization={Wolfram Research}, title={Det}, year={2024}, url={https://reference.wolfram.com/language/ref/Det.html}, note=[Accessed: 16-November-2025]}

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