performance - recursive - when to use recursion vs iteration

Recursion or Iteration? (19)

Is there a performance hit if we use loop instead of recursion or vice versa in algorithms where both can serve the same purpose? Eg : Check if given string is palindrome. I have seen many programmers using recursion as a means to show off when a simple iteration algorithm can fit the bill. Does the compiler play a vital role in deciding what to use?

As far as I know, Perl does not optimize tail-recursive calls, but you can fake it.

sub f{
  my($l,$r) = @_;

  if( $l >= $r ){
    return $l;
  } else {

    # return f( $l+1, $r );

    @_ = ( $l+1, $r );
    goto &f;


When first called it will allocate space on the stack. Then it will change its arguments, and restart the subroutine, without adding anything more to the stack. It will therefore pretend that it never called its self, changing it into an iterative process.

Note that there is no "my @_;" or "local @_;", if you did it would no longer work.

Comparing recursion to iteration is like comparing a phillips head screwdriver to a flat head screwdriver. For the most part you could remove any phillips head screw with a flat head, but it would just be easier if you used the screwdriver designed for that screw right?

Some algorithms just lend themselves to recursion because of the way they are designed (Fibonacci sequences, traversing a tree like structure, etc.). Recursion makes the algorithm more succinct and easier to understand (therefore shareable and reusable).

Also, some recursive algorithms use "Lazy Evaluation" which makes them more efficient than their iterative brothers. This means that they only do the expensive calculations at the time they are needed rather than each time the loop runs.

That should be enough to get you started. I'll dig up some articles and examples for you too.

Link 1: Haskel vs PHP (Recursion vs Iteration)

Here is an example where the programmer had to process a large data set using PHP. He shows how easy it would have been to deal with in Haskel using recursion, but since PHP had no easy way to accomplish the same method, he was forced to use iteration to get the result.

Link 2: Mastering Recursion

Most of recursion's bad reputation comes from the high costs and inefficiency in imperative languages. The author of this article talks about how to optimize recursive algorithms to make them faster and more efficient. He also goes over how to convert a traditional loop into a recursive function and the benefits of using tail-end recursion. His closing words really summed up some of my key points I think:

"recursive programming gives the programmer a better way of organizing code in a way that is both maintainable and logically consistent."

Link 3: Is recursion ever faster than looping? (Answer)

Here is a link to an answer for a question that is similar to yours. The author points out that a lot of the benchmarks associated with either recursing or looping are very language specific. Imperative languages are typically faster using a loop and slower with recursion and vice-versa for functional languages. I guess the main point to take from this link is that it is very difficult to answer the question in a language agnostic / situation blind sense.

Is recursion ever faster than looping?

I would think in (non tail) recursion there would be a performance hit for allocating a new stack etc every time the function is called (dependent on language of course).

I'm going to answer your question by designing a Haskell data structure by "induction", which is a sort of "dual" to recursion. And then I will show how this duality leads to nice things.

We introduce a type for a simple tree:

data Tree a = Branch (Tree a) (Tree a)
            | Leaf a
            deriving (Eq)

We can read this definition as saying "A tree is a Branch (which contains two trees) or is a leaf (which contains a data value)". So the leaf is a sort of minimal case. If a tree isn't a leaf, then it must be a compound tree containing two trees. These are the only cases.

Let's make a tree:

example :: Tree Int
example = Branch (Leaf 1) 
                 (Branch (Leaf 2) 
                         (Leaf 3))

Now, let's suppose we want to add 1 to each value in the tree. We can do this by calling:

addOne :: Tree Int -> Tree Int
addOne (Branch a b) = Branch (addOne a) (addOne b)
addOne (Leaf a)     = Leaf (a + 1)

First, notice that this is in fact a recursive definition. It takes the data constructors Branch and Leaf as cases (and since Leaf is minimal and these are the only possible cases), we are sure that the function will terminate.

What would it take to write addOne in an iterative style? What will looping into an arbitrary number of branches look like?

Also, this kind of recursion can often be factored out, in terms of a "functor". We can make Trees into Functors by defining:

instance Functor Tree where fmap f (Leaf a)     = Leaf (f a)
                            fmap f (Branch a b) = Branch (fmap f a) (fmap f b)

and defining:

addOne' = fmap (+1)

We can factor out other recursion schemes, such as the catamorphism (or fold) for an algebraic data type. Using a catamorphism, we can write:

addOne'' = cata go where
           go (Leaf a) = Leaf (a + 1)
           go (Branch a b) = Branch a b

If you're just iterating over a list, then sure, iterate away.

A couple of other answers have mentioned (depth-first) tree traversal. It really is such a great example, because it's a very common thing to do to a very common data structure. Recursion is extremely intuitive for this problem.

Check out the "find" methods here:

In C++ if the recursive function is a templated one, then compiler has more chance to optimize it, as all the type deduction and function instantiations will occur in compile time. Modern compilers can also inline the function if possible. So if one uses optimization flags like -O3 or -O2 in g++, then recursions may have the chance to be faster than iterations. In iterative codes, compiler get less chance to optimize it, as it is already in more or less optimal state (if written well enough).

In my case, I was trying to implement matrix exponentiation by squaring using Armadillo matrix objects, in both recursive and iterative way. Algorithm can be found here... My functions were templated and I have calculated 1,000,000 12x12 matrices raised to the power 10. I got the following result:

iterative + optimisation flag -O3 -> 2.79.. sec
recursive + optimisation flag -O3 -> 1.32.. sec

iterative + No-optimisation flag  -> 2.83.. sec
recursive + No-optimisation flag  -> 4.15.. sec

This results have been obtained using gcc-4.8 with c++11 flag (-std=c++11) and Armadillo 6.1 with Intel mkl. Intel compiler also shows similar results.

It depends on the language. In Java you should use loops. Functional languages optimize recursion.

It is possible that recursion will be more expensive, depending on if the recursive function is tail recursive (last line is recursive call). Tail recursion should be recognized by the compiler and optimized to its iterative counterpart (while maintaining the concise, clear implementation you have in your code).

I would write the algorithm in the way that makes the most sense and is the most clear for the poor sucker (be it yourself or someone else) that has to maintain the code in a few months or years. If you run into performance issues, then profile your code, and then and only then look into optimizing by moving over to an iterative implementation. You may want to look into memoization and dynamic programming.

Mike is correct. Tail recursion is not optimized out by the Java compiler or the JVM. You will always get a with something like this:

int count(int i) {
  return i >= 100000000 ? i : count(i+1);

Recursion and iteration depends on the business logic that you want to implement, though in most of the cases it can be used interchangeably. Most developers go for recursion because it is easier to understand.

Recursion is better than iteration for problems that can be broken down into multiple, smaller pieces.

For example, to make a recursive Fibonnaci algorithm, you break down fib(n) into fib(n-1) and fib(n-2) and compute both parts. Iteration only allows you to repeat a single function over and over again.

However, Fibonacci is actually a broken example and I think iteration is actually more efficient. Notice that fib(n) = fib(n-1) + fib(n-2) and fib(n-1) = fib(n-2) + fib(n-3). fib(n-1) gets calculated twice!

A better example is a recursive algorithm for a tree. The problem of analyzing the parent node can be broken down into multiple smaller problems of analyzing each child node. Unlike the Fibonacci example, the smaller problems are independent of each other.

So yeah - recursion is better than iteration for problems that can be broken down into multiple, smaller, independent, similar problems.

Recursion is more costly in memory, as each recursive call generally requires a memory address to be pushed to the stack - so that later the program could return to that point.

Still, there are many cases in which recursion is a lot more natural and readable than loops - like when working with trees. In these cases I would recommend sticking to recursion.

Recursion is very useful is some situations. For example consider the code for finding the factorial

int factorial ( int input )
  int x, fact = 1;
  for ( x = input; x > 1; x--)
     fact *= x;
  return fact;

Now consider it by using the recursive function

int factorial ( int input )
  if (input == 0)
     return 1;
  return input * factorial(input - 1);

By observing these two, we can see that recursion is easy to understand. But if it is not used with care it can be so much error prone too. Suppose if we miss if (input == 0), then the code will be executed for some time and ends with usually a .

Recursion? Where do I start, wiki will tell you “it’s the process of repeating items in a self-similar way"

Back in day when I was doing C, C++ recursion was a god send, stuff like "Tail recursion". You'll also find many sorting algorithms use recursion. Quick sort example:

Recursion is like any other algorithm useful for a specific problem. Perhaps you mightn't find a use straight away or often but there will be problem you’ll be glad it’s available.

There are many cases where it gives a much more elegant solution over the iterative method, the common example being traversal of a binary tree, so it isn't necessarily more difficult to maintain. In general, iterative versions are usually a bit faster (and during optimization may well replace a recursive version), but recursive versions are simpler to comprehend and implement correctly.

Typically, one would expect the performance penalty to lie in the other direction. Recursive calls can lead to the construction of extra stack frames; the penalty for this varies. Also, in some languages like Python (more correctly, in some implementations of some languages...), you can run into stack limits rather easily for tasks you might specify recursively, such as finding the maximum value in a tree data structure. In these cases, you really want to stick with loops.

Writing good recursive functions can reduce the performance penalty somewhat, assuming you have a compiler that optimizes tail recursions, etc. (Also double check to make sure that the function really is tail recursive---it's one of those things that many people make mistakes on.)

Apart from "edge" cases (high performance computing, very large recursion depth, etc.), it's preferable to adopt the approach that most clearly expresses your intent, is well-designed, and is maintainable. Optimize only after identifying a need.

Using recursion, you're incurring the cost of a function call with each "iteration", whereas with a loop, the only thing you usually pay is an increment/decrement. So, if the code for the loop isn't much more complicated than the code for the recursive solution, loop will usually be superior to recursion.

You have to keep in mind that utilizing too deep recursion you will run into , depending on allowed stack size. To prevent this make sure to provide some base case which ends you recursion.

it depends on "recursion depth". it depends on how much the function call overhead will influence the total execution time.

For example, calculating the classical factorial in a recursive way is very inefficient due to: - risk of data overflowing - risk of ing - function call overhead occupy 80% of execution time

while developing a min-max algorithm for position analysis in the game of chess that will analyze subsequent N moves can be implemented in recursion over the "analysis depth" (as I'm doing ^_^)