Partial Derivative Calculator
Differentiate a multivariable expression while holding the other variables constant in MiniWebtool’s Partial Derivative Calculator. Specify variables and orders, including mixed derivatives, to see the result and applied differentiation rules.
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Enter f(x, y) and the variables to differentiate, such as x or x:2,y:1, to get the partial derivative with the steps.
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.
At a Glance
Partial Derivative Calculator differentiates a multivariable expression with respect to the variables and orders you specify. It returns the result with step-by-step solutions that name the differentiation rules used.
| Feature | What you get | Good for |
|---|---|---|
| Variable syntax | Type x for a first derivative, x:2 for a second, x,y for a mixed partial, and x:2,y:1 for second in x and first in y. | Entering higher-order and mixed derivatives |
| Derivative orders | Computes first, second and higher-order partial derivatives, including mixed ones. | Multivariable calculus coursework |
| Rules named | Steps show where the power, sum, product, quotient, chain and constant multiple rules are applied. | Learning how each step was reached |
| Supported functions | Powers with ** or ^, trig and inverse trig, exp and e**x, log and ln, sqrt, hyperbolic functions, and implicit multiplication such as xy. | Typing expressions in ordinary notation |
| Example functions | Clickable examples such as x**2*y and sin(x*y) fill the input for you. | Trying the tool before entering your own function |
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:
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
First-Order Partials
Differentiate once with respect to a single variable while holding others constant. For \( f(x,y) \), these are \( f_x \) and \( f_y \).
Second-Order Partials
Differentiate twice, either \( f_{xx} \), \( f_{yy} \) (pure), or \( f_{xy} \), \( f_{yx} \) (mixed partial derivatives).
Mixed Partials
By Clairaut's theorem, if second partials are continuous, then \( f_{xy} = f_{yx} \). Order of differentiation does not matter.
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 xx:2— second derivative with respect to xx,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 | x**2, x^3, x**0.5 | 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
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