Variable arguments (varargin, varargout) in MATLAB - Deep Dive

Overview - Variable arguments (varargin, varargout)

What is it?

Variable arguments in MATLAB allow functions to accept any number of inputs or outputs. varargin collects extra input arguments into a cell array, while varargout collects extra output arguments similarly. This flexibility lets you write functions that handle different numbers of inputs or outputs without errors. It makes your functions more adaptable and reusable.

Why it matters

Without variable arguments, functions would need a fixed number of inputs and outputs, making them rigid and less useful. Real-world problems often require flexible functions that can handle varying data or options. Variable arguments solve this by letting functions adjust to different needs, saving time and reducing code duplication. This flexibility is crucial in data science where data shapes and requirements often change.

Where it fits

Before learning variable arguments, you should understand basic MATLAB functions and how to pass fixed inputs and outputs. After mastering variable arguments, you can explore advanced function handles, anonymous functions, and object-oriented programming in MATLAB. This topic is a stepping stone to writing more dynamic and powerful MATLAB code.

Mental Model

Core Idea

Variable arguments let a function treat extra inputs or outputs as flexible lists, collected in special containers, so it can handle any number of them gracefully.

Think of it like...

Imagine a backpack that can expand to hold any number of items you want to carry. You don’t have to decide in advance how many things you’ll pack; the backpack just adjusts to fit them all.

Function call with variable arguments:

┌───────────────┐
│   Function    │
│  ┌─────────┐  │
│  │ varargin│◄─── Extra inputs collected here (cell array)
│  └─────────┘  │
│  ┌─────────┐  │
│  │ varargout│─── Extra outputs collected here (cell array)
│  └─────────┘  │
└───────────────┘

Build-Up - 7 Steps

1

Foundation

Basic fixed input and output functions

Concept: Functions in MATLAB usually have a fixed number of inputs and outputs.

A simple function example: function y = square(x) y = x^2; end This function takes exactly one input and returns one output.

Result

Calling square(3) returns 9.

Understanding fixed inputs and outputs is essential before learning how to handle flexible numbers of arguments.

2

Foundation

Introduction to varargin and varargout

Concept: MATLAB provides varargin and varargout to collect extra inputs and outputs into cell arrays.

In a function definition, varargin collects all extra inputs: function myfunc(a, varargin) disp(varargin); end Similarly, varargout collects extra outputs: function varargout = myfunc() varargout{1} = 1; varargout{2} = 2; end

Result

Calling myfunc(1, 'hello', 3) displays {'hello', 3}. Calling [x,y] = myfunc() assigns x=1 and y=2.

Knowing that varargin and varargout are cell arrays helps you access any number of extra arguments flexibly.

3

Intermediate

Accessing and using varargin inputs

🤔Before reading on: Do you think varargin behaves like a normal array or a cell array? Commit to your answer.

Concept: varargin is a cell array, so you must use curly braces {} to access its elements.

Example: function greet(varargin) for k = 1:length(varargin) disp(['Hello, ' varargin{k} '!']); end end Calling greet('Alice', 'Bob') prints greetings for both names.

Result

Output: Hello, Alice! Hello, Bob!

Understanding that varargin is a cell array prevents common indexing errors and lets you handle inputs of any type.

4

Intermediate

Returning multiple outputs with varargout

🤔Before reading on: Can varargout return fewer outputs than requested? Commit to your answer.

Concept: varargout lets you return a flexible number of outputs, but you must assign outputs only up to nargout requested by the caller.

Example: function varargout = stats(x) varargout{1} = mean(x); if nargout > 1 varargout{2} = std(x); end end Calling [m, s] = stats([1 2 3]) returns mean and std. Calling m = stats([1 2 3]) returns only mean.

Result

m = 2 s = 0.8165

Knowing to check nargout avoids errors and lets your function adapt outputs to caller needs.

5

Intermediate

Combining fixed and variable arguments

Concept: You can mix fixed inputs with varargin to require some arguments and accept extra optional ones.

Example: function plotData(x, y, varargin) plot(x, y); if ~isempty(varargin) title(varargin{1}); end end Calling plotData(x, y, 'My Plot') sets the title; calling without extra args skips it.

Result

Plot appears with or without title depending on extra input.

Combining fixed and variable arguments makes functions flexible yet clear about required inputs.

6

Advanced

Handling varargin with name-value pairs

🤔Before reading on: Do you think varargin can be used to parse named options like 'Color', 'red'? Commit to your answer.

Concept: varargin can collect name-value pairs which you can parse inside the function to customize behavior.

Example: function greet(name, varargin) color = 'black'; for k = 1:2:length(varargin) if strcmpi(varargin{k}, 'Color') color = varargin{k+1}; end end disp(['Hello, ' name ', color: ' color]); end Calling greet('Alice', 'Color', 'red') prints color red.

Result

Output: Hello, Alice, color: red

Using varargin for name-value pairs enables flexible, readable function options without fixed argument order.

7

Expert

Performance and pitfalls of varargin/varargout

🤔Before reading on: Does using varargin/varargout always have zero performance cost? Commit to your answer.

Concept: varargin and varargout add overhead due to cell array handling and dynamic argument checks, which can affect performance in tight loops.

In performance-critical code, avoid excessive use of varargin/varargout or pre-parse inputs outside loops. Also, improper indexing or ignoring nargout can cause runtime errors. Example pitfall: function y = badFunc(varargin) y = varargin{1} + varargin{2}; % correct: cell array indexing end Incorrect: function y = badFunc(varargin) y = varargin(1) + varargin(2); % wrong: cell array indexing end

Result

Wrong code causes errors; correct code works as expected.

Understanding internal overhead and correct cell indexing prevents bugs and performance issues in advanced MATLAB functions.

Under the Hood

When a function declares varargin or varargout, MATLAB collects all extra input or output arguments into cell arrays at runtime. Each element of varargin or varargout is a separate cell holding one argument. The function accesses these cells using curly braces. MATLAB also tracks how many outputs the caller requests (nargout) to avoid returning more outputs than expected. This dynamic packing and unpacking happens behind the scenes, enabling flexible argument counts.

Why designed this way?

MATLAB was designed for matrix and scientific computing where functions often need flexible inputs like optional parameters or multiple outputs. Using cell arrays for variable arguments provides a uniform, type-agnostic container that can hold any data type or size. This design balances flexibility with MATLAB’s dynamic typing and ease of use. Alternatives like fixed argument lists would limit function adaptability.

┌───────────────┐
│ Function Call │
│ f(a, b, c, d)│
└──────┬────────┘
       │
       ▼
┌─────────────────────────────┐
│ Inside function:            │
│ Inputs: a, b               │
│ varargin = {c, d}          │
│ Outputs: varargout = {...} │
└─────────────────────────────┘
       ▲
       │
┌───────────────┐
│ Caller requests│
│ nargout outputs│
└───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does varargin behave like a normal array where you use parentheses to access elements? Commit yes or no.

Common Belief: varargin is just a normal array, so you can use parentheses () to access elements.

Reality: varargin is a cell array, so you must use curly braces {} to access the actual contents of each argument.

Why it matters: Using parentheses instead of curly braces causes errors or unexpected behavior, leading to bugs that are hard to debug.

Quick: Can varargout return more outputs than the caller requests? Commit yes or no.

Common Belief: varargout can return any number of outputs regardless of how many the caller asks for.

Reality: varargout must only assign outputs up to the number requested by the caller (nargout), otherwise MATLAB throws an error.

Why it matters: Ignoring nargout leads to runtime errors and crashes, breaking code reliability.

Quick: Does using varargin/varargout have zero performance cost? Commit yes or no.

Common Belief: Using varargin and varargout is free and has no impact on performance.

Reality: They add overhead due to cell array creation and dynamic argument handling, which can slow down code in tight loops or large-scale computations.

Why it matters: Not knowing this can cause unexpected slowdowns in performance-critical applications.

Quick: Can you mix fixed inputs and varargin in any order in the function signature? Commit yes or no.

Common Belief: You can place varargin anywhere in the input list of a function.

Reality: varargin must be the last input argument in the function definition.

Why it matters: Placing varargin incorrectly causes syntax errors and prevents the function from running.

Expert Zone

1

varargin and varargout are cell arrays, but their contents can be any MATLAB data type, including nested cell arrays or function handles, enabling complex flexible interfaces.

2

The nargout function inside a function returns how many outputs the caller expects, which can differ from the number of outputs the function actually defines, allowing conditional output generation.

3

Using varargin and varargout can complicate code readability and debugging, so clear documentation and input validation are essential in production code.

When NOT to use

Avoid varargin and varargout when the number of inputs and outputs is fixed or when performance is critical. Instead, use explicit input parsing with inputParser or define multiple function overloads. For very complex options, consider using structures or object-oriented approaches.

Production Patterns

In real-world MATLAB projects, varargin is often used to accept optional name-value pair arguments for configuration, while varargout is used to return multiple optional results. Functions often validate varargin contents early and provide helpful error messages. Advanced toolboxes use varargin/varargout to create flexible APIs that adapt to many use cases.

Connections

Function overloading (object-oriented programming)

Both provide ways to handle different inputs and outputs flexibly.

Understanding variable arguments helps grasp how function overloading can dispatch calls based on input types and counts.

Python *args and **kwargs

Similar pattern of collecting variable positional and keyword arguments.

Knowing MATLAB’s varargin/varargout clarifies how other languages handle flexible function arguments.

Packing and unpacking in logistics

Both involve collecting variable items into containers and unpacking them as needed.

Recognizing this pattern across domains shows how flexible packaging solves variability in inputs and outputs.

Common Pitfalls

#1 Accessing varargin elements with parentheses instead of curly braces.

Wrong approach:

function y = badFunc(varargin)
y = varargin(1) + varargin(2);
end

Correct approach:

function y = goodFunc(varargin)
y = varargin{1} + varargin{2};
end

Root cause:

Misunderstanding that varargin is a cell array requiring curly braces for content access.

#2 Assigning more outputs in varargout than requested by the caller.

Wrong approach:

function varargout = badOutputs()
varargout{1} = 1;
varargout{2} = 2;
varargout{3} = 3;
end

Called with [a,b] = badOutputs();

Correct approach:

function varargout = goodOutputs()
varargout{1} = 1;
if nargout > 1
varargout{2} = 2;
end
end

Root cause:

Ignoring nargout leads to runtime errors when returning unexpected extra outputs.

#3 Placing varargin before fixed inputs in function signature.

Wrong approach:

function myFunc(varargin, a)
disp(a);
end

Correct approach:

function myFunc(a, varargin)
disp(a);
end

Root cause:

Not knowing that varargin must be the last input argument in MATLAB function definitions.

Key Takeaways

Variable arguments in MATLAB let functions accept any number of inputs or outputs using varargin and varargout cell arrays.

varargin and varargout provide flexibility but require careful indexing with curly braces and checking nargout to avoid errors.

Combining fixed and variable arguments allows functions to require some inputs while accepting optional extras.

Using varargin for name-value pairs enables clear, flexible function options without fixed argument order.

Understanding the internal mechanics and performance costs of variable arguments helps write robust and efficient MATLAB code.