Partial Derivative Calculator

Compute partial derivatives of multivariable functions with detailed step-by-step solutions, interactive examples, and geometric visualization of tangent planes.

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Try these examples

x**2*y + y**3 w.r.t. x sin(x*y) mixed e**(x+y) second order x**2+y**2+z**2 w.r.t. x ln(x**2+y**2) mixed x*y*z triple partial

f Function f(x, y, ...)

Use ** for powers, sin, cos, exp, log, sqrt for functions

d Differentiate with respect to

x first order | x:2 second order | x,y mixed partial

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About Partial Derivative Calculator

Welcome to our Partial Derivative Calculator, a comprehensive tool for computing partial derivatives of multivariable functions with detailed step-by-step solutions. Whether you are a calculus student learning multivariable differentiation, an engineer solving optimization problems, or a scientist working with rate equations, this calculator provides accurate results with complete mathematical explanations.

What is a Partial Derivative?

A partial derivative measures how a multivariable function changes when one of its input variables changes while all other variables are held constant. Unlike ordinary derivatives which apply to single-variable functions, partial derivatives are fundamental to multivariable calculus and appear throughout science, engineering, economics, and machine learning.

Mathematical Definition

For a function $ f(x, y) $ of two variables, the partial derivative with respect to $ x $ is defined as:

Partial Derivative Definition

$$\frac{\partial f}{\partial x} = \lim_{\Delta x \to 0} \frac{f(x + \Delta x, y) - f(x, y)}{\Delta x}$$

When computing $ \frac{\partial f}{\partial x} $, we treat $ y $ as a constant and differentiate only with respect to $ x $. Similarly, $ \frac{\partial f}{\partial y} $ treats $ x $ as constant.

Key Concepts

1 First-Order Partials

Differentiate once with respect to a single variable while holding others constant. For $ f(x,y) $, these are $ f_x $ and $ f_y $.

2 Second-Order Partials

Differentiate twice, either $ f_{xx} $, $ f_{yy} $ (pure), or $ f_{xy} $, $ f_{yx} $ (mixed partial derivatives).

3 Mixed Partials

By Clairaut's theorem, if second partials are continuous, then $ f_{xy} = f_{yx} $. Order of differentiation does not matter.

4 Gradient Vector

The gradient $ \nabla f = (f_x, f_y, f_z) $ points in the direction of steepest increase. Its magnitude is the maximum rate of change.

How to Use This Calculator

Enter your function: Type a multivariable function using standard notation. Examples: x**2*y, sin(x*y), e**x * cos(y), x**3 + y**3 - 3*x*y.
Specify differentiation variables: Enter which variable(s) to differentiate with respect to:
- x — first derivative with respect to x
- x:2 — second derivative with respect to x
- x,y — mixed partial derivative (first x, then y)
- x:2,y:1 — second with respect to x, first with respect to y
Click Calculate: The calculator computes the partial derivative with a complete step-by-step solution showing which differentiation rules are applied.

Supported Functions and Syntax

Function Type	Syntax Examples	Notes
Powers	`x2`, `x^3`, `x0.5`	Use ** or ^ for exponents
Trigonometric	`sin(x)`, `cos(y)`, `tan(z)`	Also: sec, csc, cot
Inverse Trig	`asin(x)`, `atan(y)`	Also: acos, acot, asec, acsc
Exponential	`exp(x)`, `e**x`	Natural exponential function
Logarithmic	`log(x)`, `ln(x)`	Natural logarithm (base e)
Square Root	`sqrt(x)`, `x**0.5`	Equivalent forms
Hyperbolic	`sinh(x)`, `cosh(y)`, `tanh(z)`	Hyperbolic functions
Multiplication	`x*y`, `xy`, `2xy`	Implicit multiplication supported

Differentiation Rules Applied

This calculator identifies and displays which differentiation rules are used at each step:

Power Rule: $ \frac{\partial}{\partial x}(x^n) = nx^{n-1} $
Sum Rule: $ \frac{\partial}{\partial x}(f + g) = \frac{\partial f}{\partial x} + \frac{\partial g}{\partial x} $
Product Rule: $ \frac{\partial}{\partial x}(fg) = f\frac{\partial g}{\partial x} + g\frac{\partial f}{\partial x} $
Quotient Rule: $ \frac{\partial}{\partial x}\left(\frac{f}{g}\right) = \frac{g\frac{\partial f}{\partial x} - f\frac{\partial g}{\partial x}}{g^2} $
Chain Rule: $ \frac{\partial}{\partial x}f(g(x,y)) = f'(g) \cdot \frac{\partial g}{\partial x} $
Constant Multiple Rule: $ \frac{\partial}{\partial x}(cf) = c\frac{\partial f}{\partial x} $

Applications of Partial Derivatives

Gradient and Optimization

Partial derivatives form the gradient vector, which is essential for finding maxima, minima, and saddle points of multivariable functions. Setting all partial derivatives equal to zero locates critical points.

Physics and Engineering

Partial derivatives describe how physical quantities change: temperature gradients, electric potential, fluid dynamics, and wave equations all rely on partial differentiation.

Machine Learning

Gradient descent algorithms use partial derivatives to minimize loss functions. Each weight in a neural network is updated using the partial derivative of the loss with respect to that weight.

Economics

Marginal analysis uses partial derivatives to measure how output changes with respect to one input (labor, capital) while others remain fixed.

Frequently Asked Questions

What is a partial derivative?

A partial derivative measures how a multivariable function changes when one variable changes while all other variables are held constant. For a function f(x,y), the partial derivative with respect to x, written as df/dx, treats y as a constant and differentiates only with respect to x.

How do I calculate a second-order partial derivative?

To calculate a second-order partial derivative, you differentiate twice. You can differentiate twice with respect to the same variable (like d2f/dx2), or with respect to different variables (mixed partial derivative like d2f/dxdy). Enter format like 'x:2' for second derivative with respect to x, or 'x,y' for mixed partial.

What is the difference between partial and ordinary derivatives?

Ordinary derivatives apply to functions of a single variable, measuring the rate of change with respect to that one variable. Partial derivatives apply to multivariable functions and measure the rate of change with respect to one variable while treating all other variables as constants.

What is a mixed partial derivative?

A mixed partial derivative involves differentiating with respect to different variables in succession. For example, d2f/dxdy means first differentiate f with respect to y, then differentiate the result with respect to x. By Clairaut's theorem, for most functions d2f/dxdy = d2f/dydx.

How do I enter functions in the calculator?

Use standard mathematical notation: x**2 or x^2 for powers, sin(x), cos(x), tan(x) for trig functions, exp(x) or e**x for exponential, log(x) or ln(x) for natural logarithm, sqrt(x) for square root. Multiplication can be implicit (xy) or explicit (x*y).

Additional Resources

Reference this content, page, or tool as:

"Partial Derivative Calculator" at https://MiniWebtool.com/partial-derivative-calculator/ from MiniWebtool, https://MiniWebtool.com/

by miniwebtool team. Updated: Jan 19, 2026

Developer API available: Run this tool from your app, automation, or agent with one JSON HTTP request. View API docs

You can also try our AI Math Solver GPT to solve your math problems through natural language question and answer.

Partial Derivative Calculator

About Partial Derivative Calculator

What is a Partial Derivative?

Mathematical Definition

Key Concepts

1 First-Order Partials

2 Second-Order Partials

3 Mixed Partials

4 Gradient Vector

How to Use This Calculator

Supported Functions and Syntax

Differentiation Rules Applied

Applications of Partial Derivatives

Gradient and Optimization

Physics and Engineering

Machine Learning

Economics

Frequently Asked Questions

What is a partial derivative?

How do I calculate a second-order partial derivative?

What is the difference between partial and ordinary derivatives?

What is a mixed partial derivative?

How do I enter functions in the calculator?

Additional Resources

Calculus:

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