How are anonymous functions utilized in functional programming?

Anonymous functions in functional programming are used for short, one-time computations without the need for named functions.

In functional programming, anonymous functions, also known as lambda functions, are a fundamental concept. They are functions that are defined, but not bound to an identifier. This means they don't have a name and are used where they are defined. They are often used for short, one-time computations where a full function definition would be unnecessary and cumbersome.

For instance, if you need to sort a list of numbers in descending order, you could define a named function to reverse the comparison operation and pass that to the sort function. However, this would be overkill for such a simple operation. Instead, you can define an anonymous function right where you need it. This makes the code more concise and easier to understand, as the behaviour of the function is directly visible, rather than being hidden behind a function name.

Anonymous functions are also used extensively in higher-order functions. These are functions that take other functions as arguments, or return them as results. Anonymous functions are perfect for this, as they can be defined right where they are passed in as arguments. This makes the code more flexible and composable, as you can easily change the behaviour of the higher-order function by passing in different anonymous functions.

In addition, anonymous functions are used in functional programming languages to support closures. A closure is a function that has access to variables from its outer scope. This is useful for data hiding and encapsulation, as the outer variables are only accessible through the closure, not from the outside. Anonymous functions are often used to create closures, as they can be defined inside other functions and have access to their local variables.

In conclusion, anonymous functions are a powerful tool in functional programming. They allow for more concise and readable code, make higher-order functions more flexible and composable, and enable data hiding and encapsulation through closures.