Let's try writing our factorial function `int factorial(int
n)`. We want to code in the *n*! = *n**(*n* - 1)! functionality.
Easy enough:

Wasn't that easy? Lets test it to make sure it works. We call
factorial on a value of 3, `factorial(3)`:

`factorial(3)` returns `3 * factorial(2)`. But what is
`factorial(2)`?

`factorial(2)` returns `2 * factorial(1)`. And what is
`factorial(1)`?

`factorial(1)` returns `1 * factorial(0)`. But what is `factorial(0)`?

Uh oh! We messed up. Thus far

`factorial(0)`should be

`0! = 0 * factorial(-1)`. Wrong. This is a good time to talk about how one should write a recursive function, and what two cases must be considered when using recursive techniques.

There are four important criteria to think about when writing a recursive function.

- What is the base case, and can it be solved?
- What is the general case?
- Does the recursive call make the problem smaller and approach the base case?

### Base Case

The base case, or halting case, of a function is the
problem that we know the answer to, that can be solved without
any more recursive calls. The base case is what stops the
recursion from continuing on forever. Every recursive function
*must* have at least one base case (many functions have
more than one). If it doesn't, your function will not work
correctly most of the time, and will most likely cause your
program to crash in many situations, definitely not a desired
effect.

Let's return to our factorial example from above. Remember the
problem was that we never stopped the recursion process; we
didn't have a base case. Luckily, the factorial function in
math defines a base case for us. *n*! = *n**(*n* - 1)! as long as
*n* > 1. If *n* = = 1 or *n* = = 0, then *n*! = 1. The factorial
function is undefined for values less than 0, so in our
implementation, we'll return some error value. Using this
updated definition, let's rewrite our factorial function.

That's it! See how simple that was? Lets visualize what would
happen if we were to invoke this function, for example
`factorial(3)`:

### The General Case

The general case is what happens most of the time, and is where the
recursive call takes place. In the case of factorial, the general
case occurs when *n* > 1, meaning we use the equation and recursive
definition *n*! = *n**(*n* - 1)!.

### Diminishing Size of Problem

Our third requirement for a recursive function is that the on each recursive call the problem must be approaching the base case. If the problem isn't approaching the base case, we'll never reach it and the recursion will never end. Imagine the following incorrect implementation of factorial:

Note that on each recursive call, the size of `n` gets bigger, not
smaller. Since we initially start out larger than our base cases
`(n==1 & n==0)`, we will be going away from the base cases, not
towards them. Thus we will never reach them. Besides being an
incorrect implementation of the factorial algorithm, this is bad
recursive design. The recursively called problems should always be
heading towards the base case.

### Avoiding Circularity

Another problem to avoid when writing recursive functions is circularity. Circularity occurs when you reach a point in your recursion where the arguments to the function are the same as with a previous function call in the stack. If this happens you will never reach your base case, and the recursion will continue forever, or until your computer crashes, whichever comes first.

For example, let's say we had the function:

If this function is called with the value `1`, then it calls
itself with the value `2`, which in turn calls itself with
the value `1`. See the circularity?

Sometimes it is hard to determine if a function is circular. Take the Syracuse problem for example, which dates back to the 1930s.

For small values of *n*, we know that this function is not
circular, but we don't know if there is some special value of
*n* out there that causes this function to become circular.

Recursion might not be the most efficient way to implement an algorithm. Each time a function is called, there is a certain amount of "overhead" that takes up memory and system resources. When a function is called from another function, all the information about the first function must be stored so that the computer can return to it after executing the new function.

### The Call Stack

When a function is called, a certain amount of memory is set
aside for that function to use for purposes such as storing
local variables. This memory, called a frame, is also used by
the computer to store information about the function such as
the function's address in memory; this allows the program to
return to the proper place after a function call (for example,
if you write a function that calls `printf()`, you would like
control to return to your function after `printf()`
completes; this is made possible by the frame).

Every function has its own frame that is created when the function is called. Since functions can call other functions, often more than one function is in existence at any given time, and therefore there are multiple frames to keep track of. These frames are stored on the call stack, an area of memory devoted to holding information about currently running functions.

A stack is a LIFO data-type, meaning that the last item to enter the stack is the first item to leave, hence LIFO, Last In First Out. Compare this to a queue, or the line for the teller window at a bank, which is a FIFO data structure. The first people to enter the queue are the first people to leave it, hence FIFO, First In First Out. A useful example in understanding how a stack works is the pile of trays in your school's dining hall. The trays are stacked one on top of the other, and the last tray to be put on the stack is the first one to be taken off.

In the call stack, the frames are put on top of each other in
the stack. Adhering to the LIFO principle, the last function
to be called (the most recent one) is at the top of the stack
while the first function to be called (which should be the
`main()` function) resides at the bottom of the stack. When
a new function is called (meaning that the function at the top
of the stack calls another function), that new function's frame
is pushed onto the stack and becomes the active frame. When a
function finishes, its frame is destroyed and removed from the
stack, returning control to the frame just below it on the
stack (the new top frame).

Let's take an example. Suppose we have the following functions:

We can trace the flow of functions in the program by looking at
the call stack. The program begins by calling `main()` and
so the `main()` frame is placed on the stack.

`main()`function then calls the function

`stephen()`. The

`stephen()`function then calls the function

`theSpark()`. When the function

`theSpark()`is finished executing, its frame is deleted from the stack and control returns to the

`stephen()`frame. After regaining control,

`stephen()`then calls

`SparkNotes()`. When the function

`SparkNotes()`is finished executing, its frame is deleted from the stack and control returns to

`stephen()`. When

`stephen()`is finished, its frame is deleted and control returns to

`main()`. When the

`main()`function is done, it is removed from the call stack. As there are no more functions on the call stack, and thus no where to return to after

`main()`finishes, the program is finished.

### Recursion and the Call Stack

When using recursive techniques, functions "call themselves".
If the function `stephen()` were recursive, `stephen()`
might make a call to `stephen()` during the course of its
execution. However, as mentioned before, it is important to
realize that every function called gets its own frame, with its
own local variables, its own address, etc. As far as the
computer is concerned, a recursive call is just like any other
call.

Changing the example from above, let's say the `stephen`
function calls itself. When the program begins, a frame for
`main()` is placed on the call stack.
`main()` then calls `stephen()` which is placed on the stack.

`stephen()`then makes a recursive call to itself, creating a new frame which is placed on the stack.

### Overhead of Recursion

Imagine what happens when you call the factorial function on some large input, say 1000. The first function will be called with input 1000. It will call the factorial function on an input of 999, which will call the factorial function on an input of 998. Etc. Keeping track of the information about all active functions can use many system resources if the recursion goes many levels deep. In addition, functions take a small amount of time to be instantiated, to be set up. If you have a lot of function calls in comparison to the amount of work each one is actually doing, your program will run significantly slower.

So what can be done about this? You'll need to decide up front whether recursion is necessary. Often, you'll decide that an iterative implementation would be more efficient and almost as easy to code (sometimes they'll be easier, but rarely). It has been proven mathematically that any problem that can be solved with recursion can also be solved with iteration, and vice versa. However, there are certainly cases where recursion is a blessing, and in these instances you should not shy away from using it. As we'll see later, recursion is often a useful tool when working with data structures such as trees (if you have no experience with trees, please see the SparkNote on the subject).

As an example of how a function can be written both recursively and iteratively, let's look again to the factorial function.

We originally said that the 5! = 5*4*3*2*1 and 9! = 9*8*7*6*5*4*3*2*1. Let's use this definition instead of the recursive one to write our function iteratively. The factorial of an integer is that number multiplied by all integers smaller than it and greater than 0.

This program is more efficient and should execute faster than the recursive solution above.

For mathematical problems like factorial, there is sometimes an alternative to both an iterative and a recursive implementation: a closed-form solution. A closed-form solution is a formula that involves no looping of any kind, only standard mathematical operations in a formula to compute the answer. The Fibonacci function, for example, does have a closed-form solution:

This solution and implementation uses four calls to `sqrt()`,
two calls to `pow()`, two additions, two subtractions, two
multiplications, and four divisions. One might argue that this
is more efficient than both the recursive and iterative
solutions for large values of *n*. Those solutions involve a
lot of looping/repetition, while this solution does not.
However, without the source code for `pow()`, it is
impossible to say that this is more efficient. Most likely,
the bulk of the cost of this function is in the calls to
`pow()`. If the programmer for `pow()` wasn't smart about
the algorithm, it could have as many as *n* - 1 multiplications,
which would make this solution slower than the iterative, and
possibly even the recursive, implementation.

Given that recursion is in general less efficient, why would we use it? There are two situations where recursion is the best solution:

- The problem is much more clearly solved using recursion: there are many problems where the recursive solution is clearer, cleaner, and much more understandable. As long as the efficiency is not the primary concern, or if the efficiencies of the various solutions are comparable, then you should use the recursive solution.
- Some problems are much easier to solve through recursion: there are some problems which do not have an easy iterative solution. Here you should use recursion. The Towers of Hanoi problem is an example of a problem where an iterative solution would be very difficult. We'll look at Towers of Hanoi in a later section of this guide.