Jacobian matrix and determinant

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In vector calculus, the Jacobian matrix (/dʒᵻˈkoʊbiən/, /jᵻˈkoʊbiən/) is the matrix of all first-order partial derivatives of a vector-valued function. When the matrix is a square matrix, both the matrix and its determinant are referred to as the Jacobian in literature.^[1]

Suppose $f : ℝ n \to ℝ m$ is a function which takes as input the vector $x \in ℝ n$ and produces as output the vector $f (x) \in ℝ m$ . Then the Jacobian matrix $J$ of $f$ is an $m \times n$ matrix, usually defined and arranged as follows:

\mathbf {J} ={\begin{bmatrix}{\dfrac {\partial \mathbf {f} }{\partial x_{1}}}&\cdots &{\dfrac {\partial \mathbf {f} }{\partial x_{n}}}\end{bmatrix}}={\begin{bmatrix}{\dfrac {\partial f_{1}}{\partial x_{1}}}&\cdots &{\dfrac {\partial f_{1}}{\partial x_{n}}}\\\vdots &\ddots &\vdots \\{\dfrac {\partial f_{m}}{\partial x_{1}}}&\cdots &{\dfrac {\partial f_{m}}{\partial x_{n}}}\end{bmatrix}}

or, component-wise:

\mathbf {J} _{ij}={\frac {\partial f_{i}}{\partial x_{j}}}.

This matrix, whose entries are functions of $x$ , is also denoted by $D f$ , $J f$ , and ∂(f₁,...,f_m)/∂(x₁,...,x_n). (Note that some literature defines the Jacobian as the transpose of the matrix given above.)

The Jacobian matrix is important because if the function $f$ is differentiable at a point $x$ (this is a slightly stronger condition than merely requiring that all partial derivatives exist there), then the Jacobian matrix defines a linear map $ℝ n \to ℝ m$ , which is the best (pointwise) linear approximation of the function $f$ near the point $x$ . This linear map is thus the generalization of the usual notion of derivative, and is called the derivative or the differential of $f$ at $x$ .

If $m$ = $n$ , the Jacobian matrix is a square matrix, and its determinant, a function of $x 1, \dots, x n$ , is the Jacobian determinant of $f$ . It carries important information about the local behavior of $f$ . In particular, the function $f$ has locally in the neighborhood of a point $x$ an inverse function that is differentiable if and only if the Jacobian determinant is nonzero at $x$ (see Jacobian conjecture). The Jacobian determinant also appears when changing the variables in multiple integrals (see substitution rule for multiple variables).

If $m$ = 1, $f$ is a scalar field and the Jacobian matrix is reduced to a row vector of partial derivatives of $f$ —i.e. the gradient of $f$ .

These concepts are named after the mathematician Carl Gustav Jacob Jacobi (1804–1851).

Jacobian matrix

The Jacobian generalizes the gradient of a scalar-valued function of multiple variables, which itself generalizes the derivative of a scalar-valued function of a single variable. In other words, the Jacobian for a scalar-valued multivariate function is the gradient and that of a scalar-valued function of single variable is simply its derivative. The Jacobian can also be thought of as describing the amount of "stretching", "rotating" or "transforming" that a transformation imposes locally. For example, if $(x', y') = f (x, y)$ is used to transform an image, the Jacobian $J f (x, y)$ , describes how the image in the neighborhood of $(x, y)$ is transformed.

If a function is differentiable at a point, its derivative is given in coordinates by the Jacobian, but a function doesn't need to be differentiable for the Jacobian to be defined, since only the partial derivatives are required to exist.

If $p$ is a point in $ℝ n$ and $f$ is differentiable at $p$ , then its derivative is given by $J f (p)$ . In this case, the linear map described by $J f (p)$ is the best linear approximation of $f$ near the point $p$ , in the sense that

\mathbf {f} (\mathbf {x} )=\mathbf {f} (\mathbf {p} )+\mathbf {J} _{\mathbf {f} }(\mathbf {p} )(\mathbf {x} -\mathbf {p} )+o(\|\mathbf {x} -\mathbf {p} \|)

for $x$ close to $p$ and where $o$ is the little o-notation (for $x \to p$ ) and $‖ x - p ‖$ is the distance between $x$ and $p$ .

Compare this to a Taylor series for a scalar function of a scalar argument, truncated to first order:

f(x)=f(p)+f'(p)(x-p)+o(x-p).

In a sense, both the gradient and Jacobian are "first derivatives"—the former the first derivative of a scalar function of several variables, the latter the first derivative of a vector function of several variables.

The Jacobian of the gradient of a scalar function of several variables has a special name: the Hessian matrix, which in a sense is the "second derivative" of the function in question.

Jacobian determinant

A nonlinear map f : R² → R² sends a small square to a distorted parallelogram close to the image of the square under the best linear approximation of f near the point.

If $m$ = $n$ , then $f$ is a function from $ℝ n$ to itself and the Jacobian matrix is a square matrix. We can then form its determinant, known as the Jacobian determinant. The Jacobian determinant is occasionally referred to as "the Jacobian".

The Jacobian determinant at a given point gives important information about the behavior of $f$ near that point. For instance, the continuously differentiable function $f$ is invertible near a point $p \in ℝ n$ if the Jacobian determinant at $p$ is non-zero. This is the inverse function theorem. Furthermore, if the Jacobian determinant at $p$ is positive, then $f$ preserves orientation near $p$ ; if it is negative, $f$ reverses orientation. The absolute value of the Jacobian determinant at $p$ gives us the factor by which the function $f$ expands or shrinks volumes near $p$ ; this is why it occurs in the general substitution rule.

The Jacobian determinant is used when making a change of variables when evaluating a multiple integral of a function over a region within its domain. To accommodate for the change of coordinates the magnitude of the Jacobian determinant arises as a multiplicative factor within the integral. This is because the $n$ -dimensional $dV$ element is in general a parallelepiped in the new coordinate system, and the $n$ -volume of a parallelepiped is the determinant of its edge vectors.

The Jacobian can also be used to solve systems of differential equations at an equilibrium point or approximate solutions near an equilibrium point.

Inverse

According to the inverse function theorem, the matrix inverse of the Jacobian matrix of an invertible function is the Jacobian matrix of the inverse function. That is, if the Jacobian of the function $f : ℝ n \to ℝ n$ is continuous and nonsingular at the point $p$ in $ℝ n$ , then $f$ is invertible when restricted to some neighborhood of $p$ and

\mathbf {J} _{\mathbf {f} ^{-1}}\circ \mathbf {f} ={\mathbf {J} _{\mathbf {f} }}^{-1}.

Conversely, if the Jacobian determinant is not zero at a point, then the function is locally invertible near this point, that is, there is a neighbourhood of this point in which the function is invertible.

The (unproved) Jacobian conjecture is related to global invertibility in the case of a polynomial function, that is a function defined by n polynomials in n variables. It asserts that, if the Jacobian determinant is a non-zero constant (or, equivalently, that it does not have any complex zero), then the function is invertible and its inverse is a polynomial function.

Critical points

Main article: Critical point

If $f : ℝ n \to ℝ m$ is a differentiable function, a critical point of $f$ is a point where the rank of the Jacobian matrix is not maximal. This means that the rank at the critical point is lower than the rank at some neighbour point. In other words, let $k$ be the maximal dimension of the open balls contained in the image of $f$ ; then a point is critical if all minors of rank $k$ of $f$ are zero.

In the case where 1 = $m$ = $n$ = $k$ , a point is critical if the Jacobian determinant is zero.

Examples

Example 1

Consider the function $f : ℝ 2 \to ℝ 2$ given by

\mathbf {f} (x,y)={\begin{bmatrix}x^{2}y\\5x+\sin y\end{bmatrix}}.

Then we have

f_{1}(x,y)=x^{2}y

and

f_{2}(x,y)=5x+\sin y

and the Jacobian matrix of $F$ is

\mathbf {J} _{\mathbf {f} }(x,y)={\begin{bmatrix}{\dfrac {\partial f_{1}}{\partial x}}&{\dfrac {\partial f_{1}}{\partial y}}\\[1em]{\dfrac {\partial f_{2}}{\partial x}}&{\dfrac {\partial f_{2}}{\partial y}}\end{bmatrix}}={\begin{bmatrix}2xy&x^{2}\\5&\cos y\end{bmatrix}}

and the Jacobian determinant is

\det(\mathbf {J} _{\mathbf {f} }(x,y))=2xy\cos y-5x^{2}.

Example 2: polar-Cartesian transformation

The transformation from polar coordinates $(r, φ)$ to Cartesian coordinates (x, y), is given by the function $F : ℝ + \times [0, 2 π) \to ℝ 2$ with components:

{\begin{aligned}x&=r\cos \varphi ;\\y&=r\sin \varphi .\end{aligned}}

\mathbf {J} (r,\varphi )={\begin{bmatrix}{\dfrac {\partial x}{\partial r}}&{\dfrac {\partial x}{\partial \varphi }}\\[1em]{\dfrac {\partial y}{\partial r}}&{\dfrac {\partial y}{\partial \varphi }}\end{bmatrix}}={\begin{bmatrix}\cos \varphi &-r\sin \varphi \\\sin \varphi &r\cos \varphi \end{bmatrix}}

The Jacobian determinant is equal to $r$ . This can be used to transform integrals between the two coordinate systems:

\iint _{A}f(x,y)\,dx\,dy=\iint _{A}f(r\cos \varphi ,r\sin \varphi )\,r\,dr\,d\varphi .

Example 3: spherical-Cartesian transformation

The transformation from spherical coordinates $(r, θ, φ)$ to Cartesian coordinates (x, y, z), is given by the function $F : ℝ + \times [0, π] \times [0, 2 π) \to ℝ 3$ with components:

{\begin{aligned}x&=r\sin \theta \cos \varphi ;\\y&=r\sin \theta \sin \varphi ;\\z&=r\cos \theta .\end{aligned}}

The Jacobian matrix for this coordinate change is

\mathbf {J} _{\mathbf {F} }(r,\theta ,\varphi )={\begin{bmatrix}{\dfrac {\partial x}{\partial r}}&{\dfrac {\partial x}{\partial \theta }}&{\dfrac {\partial x}{\partial \varphi }}\\[1em]{\dfrac {\partial y}{\partial r}}&{\dfrac {\partial y}{\partial \theta }}&{\dfrac {\partial y}{\partial \varphi }}\\[1em]{\dfrac {\partial z}{\partial r}}&{\dfrac {\partial z}{\partial \theta }}&{\dfrac {\partial z}{\partial \varphi }}\end{bmatrix}}={\begin{bmatrix}\sin \theta \cos \varphi &r\cos \theta \cos \varphi &-r\sin \theta \sin \varphi \\\sin \theta \sin \varphi &r\cos \theta \sin \varphi &r\sin \theta \cos \varphi \\\cos \theta &-r\sin \theta &0\end{bmatrix}}.

The determinant is $r 2 sin θ$ . As an example, since $dV = dx dy dz$ this determinant implies that the differential volume element $dV = r 2 sin θ dr dθ dφ$ . Nevertheless this determinant varies with coordinates.

Example 4

The Jacobian matrix of the function $F : ℝ 3 \to ℝ 4$ with components

{\begin{aligned}y_{1}&=x_{1}\\y_{2}&=5x_{3}\\y_{3}&=4x_{2}^{2}-2x_{3}\\y_{4}&=x_{3}\sin x_{1}\end{aligned}}

\mathbf {J} _{\mathbf {F} }(x_{1},x_{2},x_{3})={\begin{bmatrix}{\dfrac {\partial y_{1}}{\partial x_{1}}}&{\dfrac {\partial y_{1}}{\partial x_{2}}}&{\dfrac {\partial y_{1}}{\partial x_{3}}}\\[1em]{\dfrac {\partial y_{2}}{\partial x_{1}}}&{\dfrac {\partial y_{2}}{\partial x_{2}}}&{\dfrac {\partial y_{2}}{\partial x_{3}}}\\[1em]{\dfrac {\partial y_{3}}{\partial x_{1}}}&{\dfrac {\partial y_{3}}{\partial x_{2}}}&{\dfrac {\partial y_{3}}{\partial x_{3}}}\\[1em]{\dfrac {\partial y_{4}}{\partial x_{1}}}&{\dfrac {\partial y_{4}}{\partial x_{2}}}&{\dfrac {\partial y_{4}}{\partial x_{3}}}\end{bmatrix}}={\begin{bmatrix}1&0&0\\0&0&5\\0&8x_{2}&-2\\x_{3}\cos x_{1}&0&\sin x_{1}\end{bmatrix}}.

This example shows that the Jacobian need not be a square matrix.

Example 5

The Jacobian determinant of the function $F : ℝ 3 \to ℝ 3$ with components

{\begin{aligned}y_{1}&=5x_{2}\\y_{2}&=4x_{1}^{2}-2\sin(x_{2}x_{3})\\y_{3}&=x_{2}x_{3}\end{aligned}}

{\begin{vmatrix}0&5&0\\8x_{1}&-2x_{3}\cos(x_{2}x_{3})&-2x_{2}\cos(x_{2}x_{3})\\0&x_{3}&x_{2}\end{vmatrix}}=-8x_{1}{\begin{vmatrix}5&0\\x_{3}&x_{2}\end{vmatrix}}=-40x_{1}x_{2}.

From this we see that $F$ reverses orientation near those points where $x 1$ and $x 2$ have the same sign; the function is locally invertible everywhere except near points where $x 1 = 0$ or $x 2 = 0$ . Intuitively, if one starts with a tiny object around the point $(1, 2, 3)$ and apply $F$ to that object, one will get a resulting object with approximately $40 \times 1 \times 2 = 80$ times the volume of the original one, with orientation reversed.

Other uses

The Jacobian serves as a linearized design matrix in statistical regression and curve fitting; see non-linear least squares.

Dynamical systems

Consider a dynamical system of the form $x' = F (x)$ , where $x'$ is the (component-wise) time derivative of $x$ , and $F : ℝ n \to ℝ n$ is differentiable. If $F (x 0) = 0$ , then $x 0$ is a stationary point (also called a critical point; this is not to be confused with fixed points). The behavior of the system near a stationary point is related to the eigenvalues of $J F (x 0)$ , the Jacobian of $F$ at the stationary point.^[2] Specifically, if the eigenvalues all have real parts that are negative, then the system is stable near the stationary point, if any eigenvalue has a real part that is positive, then the point is unstable. If the largest real part of the eigenvalues is zero, the Jacobian matrix does not allow for an evaluation of the stability.

Newton's method

A system of coupled nonlinear equations can be solved iteratively by Newton's method. This method uses the Jacobian matrix of the system of equations.

References

↑ Mathworld
↑ Arrowsmith, D. K.; Place, C. M. (1992). "Section 3.3". Dynamical Systems. London: Chapman & Hall. ISBN 0-412-39080-9.

External links

Hazewinkel, Michiel, ed. (2001), "Jacobian", Encyclopedia of Mathematics, Springer, ISBN 978-1-55608-010-4
Mathworld A more technical explanation of Jacobians

Matrix classes

Explicitly constrained entries	(0,1) Alternant Anti-diagonal Anti-Hermitian Anti-symmetric Arrowhead Band Bidiagonal Binary Bisymmetric Block-diagonal Block Block tridiagonal Boolean Cauchy Centrosymmetric Conference Complex Hadamard Copositive Diagonally dominant Diagonal Discrete Fourier Transform Elementary Equivalent Frobenius Generalized permutation Hadamard Hankel Hermitian Hessenberg Hollow Integer Logical Markov Metzler Monomial Moore Nonnegative Partitioned Parisi Pentadiagonal Permutation Persymmetric Polynomial Positive Quaternionic Sign Signature Skew-Hermitian Skew-symmetric Skyline Sparse Sylvester Symmetric Toeplitz Triangular Tridiagonal Unitary Vandermonde Walsh Z

Constant	Exchange Hilbert Identity Lehmer Of ones Pascal Pauli Redheffer Shift Zero

Conditions on eigenvalues or eigenvectors	Companion Convergent Defective Diagonalizable Hurwitz Positive-definite Stability Stieltjes

Satisfying conditions on products or inverses	Congruent Idempotent or Projection Invertible Involutory Nilpotent Normal Orthogonal Orthonormal Singular Unimodular Unipotent Totally unimodular Weighing

With specific applications	Adjugate Alternating sign Augmented Bézout Carleman Cartan Circulant Cofactor Commutation Coxeter Derogatory Distance Duplication Elimination Euclidean distance Fundamental (linear differential equation) Generator Gramian Hessian Householder Jacobian Moment Payoff Pick Random Rotation Seifert Shear Similarity Symplectic Totally positive Transformation Wedderburn X–Y–Z

Used in statistics	Bernoulli Centering Correlation Covariance Dispersion Doubly stochastic Fisher information Hat Precision Stochastic Transition

Used in graph theory	Adjacency Biadjacency Degree Edmonds Incidence Laplacian Seidel adjacency Skew-adjacency Tutte

Used in science and engineering	Cabibbo–Kobayashi–Maskawa Density Fundamental (computer vision) Fuzzy associative Gamma Gell-Mann Hamiltonian Irregular Overlap S State transition Substitution Z (chemistry)

Related terms	Jordan canonical form Linear independence Matrix exponential Matrix representation of conic sections Perfect matrix Pseudoinverse Quaternionic matrix Row echelon form Wronskian

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