In vector calculus, the Jacobian is shorthand for either the Jacobian matrix or its determinant, the Jacobian determinant.

Also, in algebraic geometry the Jacobian of a curve means the Jacobian variety: a group structure, which can be imposed on the curve.

They are all named after the mathematician Carl Gustav Jacobi; the term "Jacobian" may be pronounced as "yah-KO-bee-un".

Jacobian matrix

The Jacobian matrix is the matrix of all first-order partial derivatives of a vector-valued function. Its importance lies in the fact that it represents the best linear approximation to a differentiable function near a given point. In this sense, the Jacobian is akin to a derivative of a multivariate function.

Suppose F : Rⁿ → R^m is a function from Euclidean n-space to Euclidean m-space. Such a function is given by m real-valued component functions, y₁(x₁,...,x_n), ..., y_m(x₁,...,x_n). The partial derivatives of all these functions (if they exist) can be organized in an m-by-n matrix, the Jacobian matrix of F, as follows:

This matrix is denoted by

The i-th row of this matrix is given by the gradient of the function y_i for i=1,...,m.

If p is a point in Rⁿ and F is differentiable at p, then its derivative is given by J_F(p) (and this is the easiest way to compute said derivative). 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

for x close to p.

Example

The Jacobian matrix of the function F : R³ → R⁴ with components:

y₁ = x₁

y₂ = 5x₃

y₃ = 4(x₂)² - 2x₃

y₄ = x₃sin(x₁)

is:

Jacobian determinant

If m = n, then F is a function from n-space to n-space and the Jacobi matrix is a square matrix. We can then form its determinant, known as the Jacobian determinant.

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 p if and only 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.

Example

The Jacobian determinant of the function F : R³ → R³ with components

y₁ = 5x₂

y₂ = 4(x₁)² - 2sin(x₂x₃)

y₃ = x₂x₃

is:

From this we see that F reverses orientation near those points where x₁ and x₂ have the same sign; the function is locally invertible everywhere except near points where x₁=0 or x₂=0. If you start with a tiny object around the point (1,1,1) and apply F to that object, you will get an object set with about 40 times the volume of the original one.