Jacobian Matrix Calculator

Calculate the Jacobian matrix of a vector-valued multivariable function. Enter the transformation components to get every partial derivative, the determinant, eigenvalues, worked steps and a grid deformation visualization.

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About Jacobian Matrix Calculator

The Jacobian Matrix Calculator computes the Jacobian matrix of any vector-valued multivariable function. Enter transformation components like $F(x,y) = (x^2 + y,\; xy)$, specify your variables, and optionally evaluate at a specific point. The tool returns the full symbolic Jacobian matrix, determinant, eigenvalues, a step-by-step MathJax solution, and for 2×2 cases, an interactive grid deformation visualization showing how the linear transformation stretches, rotates, and shears space.

What Is the Jacobian Matrix?

The Jacobian matrix of a vector-valued function $\mathbf{F}: \mathbb{R}^n \to \mathbb{R}^m$ is the $m \times n$ matrix of all first-order partial derivatives:

$$J = \begin{pmatrix} \frac{\partial F_1}{\partial x_1} & \cdots & \frac{\partial F_1}{\partial x_n} \\ \vdots & \ddots & \vdots \\ \frac{\partial F_m}{\partial x_1} & \cdots & \frac{\partial F_m}{\partial x_n} \end{pmatrix}$$

The Jacobian represents the best linear approximation of the function near a given point. It generalizes the concept of a derivative to vector-valued functions of several variables.

Key Concepts

⊞

Jacobian Matrix

The m×n matrix of all partial derivatives ∂Fᵢ/∂xⱼ — the total derivative in matrix form.

◇

Determinant

For square J: det(J) measures local area/volume scaling and orientation change.

↔

Inverse Function Theorem

If det(J) ≠ 0 at a point, the function is locally invertible there.

∮

Change of Variables

In integration: dA' = |det(J)| dA accounts for coordinate stretching.

The Jacobian Determinant

When the Jacobian matrix is square ($m = n$), its determinant has deep geometric meaning:

det(J)	Geometric Meaning	Example
det(J) > 0	Orientation preserved, area scaled by det(J)	Expansion, rotation
det(J) < 0	Orientation reversed, area scaled by \|det(J)\|	Reflection
det(J) = 0	Singular — a dimension collapses, not locally invertible	Projection to lower dimension
\|det(J)\| = 1	Area/volume preserved (isometry or rotation)	Rotation matrix

Common Coordinate Transformations

Transformation	Mapping	Jacobian Determinant
Polar → Cartesian	$x = r\cos\theta,\; y = r\sin\theta$	$r$
Cylindrical → Cartesian	$x = r\cos\theta,\; y = r\sin\theta,\; z = z$	$r$
Spherical → Cartesian	$x = r\sin\phi\cos\theta,\; y = r\sin\phi\sin\theta,\; z = r\cos\phi$	$r^2 \sin\phi$
2D Rotation by α	$x' = x\cos\alpha - y\sin\alpha,\; y' = x\sin\alpha + y\cos\alpha$	1
Scaling	$x' = ax,\; y' = by$	$ab$

Applications of the Jacobian

Field	Application	Role of the Jacobian
Multivariable Calculus	Change of variables in integrals	\|det(J)\| is the scaling factor for area/volume elements
Robotics	Robot arm kinematics	Maps joint velocities to end-effector velocities
Machine Learning	Normalizing flows	det(J) computes probability density change through transformations
Physics	Coordinate transformations	Tensor transformation laws, metric tensors
Optimization	Newton's method (multivariate)	Jacobian of the gradient = Hessian; used in convergence analysis
Computer Graphics	Texture mapping, mesh deformation	Measures distortion when mapping between surfaces

How to Use the Jacobian Matrix Calculator

Enter function components: Type each component of your vector-valued function separated by semicolons. For example, x^2 + y; x*y for $\mathbf{F}(x,y) = (x^2+y, xy)$. Use ^ for exponents, * for multiplication, and standard functions like sin, cos, exp, ln, sqrt.
Specify variables: Enter variable names separated by commas (e.g., x, y or r, t). The number of variables determines the number of columns in the Jacobian.
Enter an evaluation point (optional): Provide coordinate values to evaluate the Jacobian numerically. You can use constants like pi and e.
Click Compute Jacobian: View the symbolic Jacobian matrix, all partial derivatives, the determinant (for square matrices), eigenvalues, and the step-by-step solution.
Explore the visualization: For 2×2 Jacobians, see the interactive grid deformation showing how the matrix transforms the original grid, unit circle, and basis vectors. Toggle between Grid, Circle, and Both views.

Worked Example: Polar Coordinates

Find the Jacobian of the polar-to-Cartesian transformation $F(r, \theta) = (r\cos\theta,\; r\sin\theta)$:

Step 1: Compute partial derivatives: $\frac{\partial F_1}{\partial r} = \cos\theta$, $\frac{\partial F_1}{\partial \theta} = -r\sin\theta$, $\frac{\partial F_2}{\partial r} = \sin\theta$, $\frac{\partial F_2}{\partial \theta} = r\cos\theta$.

Step 2: Assemble: $J = \begin{pmatrix} \cos\theta & -r\sin\theta \\ \sin\theta & r\cos\theta \end{pmatrix}$

Step 3: Determinant: $\det(J) = r\cos^2\theta + r\sin^2\theta = r$. This is why the area element in polar coordinates is $r\,dr\,d\theta$.

Relationship to Other Concepts

The Jacobian matrix connects to many fundamental concepts in mathematics:

Gradient: For a scalar function $f: \mathbb{R}^n \to \mathbb{R}$, the Jacobian is a $1 \times n$ row vector — the transpose of the gradient $\nabla f$.
Hessian: The Hessian matrix is the Jacobian of the gradient: $H(f) = J(\nabla f)$.
Divergence and Curl: The divergence is the trace of the Jacobian; the curl involves off-diagonal antisymmetric components.
Chain Rule: For composite functions, $J(\mathbf{G} \circ \mathbf{F}) = J(\mathbf{G}) \cdot J(\mathbf{F})$ — the chain rule becomes matrix multiplication of Jacobians.

FAQ

What is the Jacobian matrix?

The Jacobian matrix is the matrix of all first-order partial derivatives of a vector-valued function. For a function F from R^n to R^m, the Jacobian is an m by n matrix where entry (i,j) is the partial derivative of the i-th output with respect to the j-th input variable. It represents the best linear approximation of the function near a point and is fundamental to multivariable calculus, differential equations, and many areas of applied mathematics.

What does the Jacobian determinant represent?

The Jacobian determinant (for square Jacobians) measures how much the transformation locally scales area or volume. A determinant of 2 means areas double, a determinant of -1 means areas are preserved but orientation is reversed, and a determinant of 0 means the transformation collapses a dimension at that point. In integration, the absolute value of the Jacobian determinant is used as the scaling factor when changing variables.

How is the Jacobian used in coordinate transformations?

In multivariable calculus, when changing variables in a multiple integral, the Jacobian determinant appears as a scaling factor. For example, converting from Cartesian to polar coordinates requires multiplying by the absolute value of the Jacobian determinant |r|, giving the familiar area element r dr dθ. This accounts for how infinitesimal area or volume elements change under the coordinate mapping.

What is the difference between the Jacobian matrix and the Hessian matrix?

The Jacobian is the matrix of first derivatives of a vector-valued function (maps R^n to R^m). The Hessian is the matrix of second derivatives of a scalar-valued function (maps R^n to R). The Hessian of a scalar function f equals the Jacobian of its gradient: H(f) = J(∇f). The Jacobian describes how a transformation stretches space; the Hessian describes the curvature of a surface.

When is the Jacobian matrix invertible?

The Jacobian matrix is invertible at a point when it is square (m = n) and its determinant is nonzero. By the Inverse Function Theorem, if the Jacobian is invertible at a point, the function is locally invertible near that point, meaning there exists a smooth local inverse function. This is a cornerstone result in differential topology and is used extensively in implicit function theory and nonlinear analysis.

Reference this content, page, or tool as:

"Jacobian Matrix Calculator" at https://MiniWebtool.com/jacobian-matrix-calculator/ from MiniWebtool, https://MiniWebtool.com/

by miniwebtool team. Updated: 2026-04-08

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Transformation	Mapping	Jacobian Determinant
Polar → Cartesian	\(x = r\cos\theta,\; y = r\sin\theta\)	\(r\)
Cylindrical → Cartesian	\(x = r\cos\theta,\; y = r\sin\theta,\; z = z\)	\(r\)
Spherical → Cartesian	\(x = r\sin\phi\cos\theta,\; y = r\sin\phi\sin\theta,\; z = r\cos\phi\)	\(r^2 \sin\phi\)
2D Rotation by α	\(x' = x\cos\alpha - y\sin\alpha,\; y' = x\sin\alpha + y\cos\alpha\)	1
Scaling	\(x' = ax,\; y' = by\)	\(ab\)