How do I analyze this recursive function on paper? - recursion

void recursive(int n) {
if (n<=0) {
return;
}
printf("%d ",n);
recursive(n-2);
recursive(n-2);
printf("%d ",n);
}
So my question is: how do I go about determining the output of this piece of code (if we assume n=3 initially) without any tools but a pen and paper? Is there any technique for notating different levels of the recursive call, because I keep getting lost in trying to wrap my head around this. Please help!

Is there any technique for notating different levels of the recursive call
Indentation. Graph paper makes that easier. Leave the first k squares empty in each line to indicate you are at recursion depth k.

I would try to think about this as a tree of the calls. In this particular case as int n is passed by value you can just duplicate the descendants for the first recursive call.
When n=3
First n will be printed equaling 3
Then recursive will be called with n=1 so then it will print 1
Then recursive will be called with n=-1. This will trigger the direct return statement resulting in no output.
At the end the original number is output
The above will happen twice for each original recursive call resulting in the following output.
3
1
1
1
1
3

Related

Recursion in java help

I am new to the site and am not familiar with how and where to post so please excuse me. I am currently studying recursion and am having trouble understanding the output of this program. Below is the method body.
public static int Asterisk(int n)
{
if (n<1)
return;
Asterisk(n-1);
for (int i = 0; i<n; i++)
{
System.out.print("*");
}
System.out.println();
}
This is the output
*
**
***
****
*****
it is due to the fact that the "Asterisk(n-1)" lies before the for loop.
I would think that the output should be
****
***
**
*
This is the way head recursion works. The call to the function is made before execution of other statements. So, Asterisk(5) calls Asterisk(4) before doing anything else. This further cascades into serial function calls from Asterisk(3) → Asterisk(2) → Asterisk(1) → Asterisk(0).
Now, Asterisk(0) simply returns as it passes the condition n<1. The control goes back to Asterisk(1) which now executes the rest of its code by printing n=1 stars. Then it relinquishes control to Asterisk(2) which again prints n=2 stars, and so on. Finally, Asterisk(5) prints its n=5 stars and the function calls end. This is why you see the pattern of ascending number of stars.
There are two ways to create programming loops. One is using imperative loops normally native to the language (for, while, etc) and the other is using functions (functional loops). In your example the two kinds of loops are presented.
One loop is the unrolling of the function
Asterisk(int n)
This unrolling uses recursion, where the function calls itself. Every functional loop must know when to stop, otherwise it goes on forever and blows up the stack. This is called the "stopping condition". In your case it is :
if (n<1)
return;
There is bidirectional equivalence between functional loops and imperative loops (for, while, etc). You can turn any functional loop into a regular loop and vice versa.
IMO this particular exercise was meant to show you the two different ways to build loops. The outer loop is functional (you could substitute it for a for loop) and the inner loop is imperative.
Think of recursive calls in terms of a stack. A stack is a data structure which adds to the top of a pile. A real world analogy is a pile of dishes where the newest dish goes on the top. Therefore recursive calls add another layer to the top of the stack, then once some criteria is met which prevents further recursive calls, the stack starts to unwind and we work our way back down to the original item (the first plate in pile of dishes).
The input of a recursive method tends towards a base case which is the termination factor and prevents the method from calling itself indefinitely (infinite loop). Once this base condition is met the method returns a value rather than calling itself again. This is how the stack in unwound.
In your method, the base case is when $n<1$ and the recursive calls use the input $n-1$. This means the method will call itself, each time decreasing $n$ by 1, until $n<1$ i.e. $n=0$. Once the base condition is met, the value 0 is returned and we start to execute the $for$ loop. This is why the first line contains a single asterix.
So if you run the method with an input of 5, the recursive calls build a stack of values of $n$ as so
0
1
2
3
4
5
Then this stack is unwound starting with the top, 0, all the way down to 5.

explanation about inner functioning of recursion

I have seen the following piece of code:
1. void f(int n){
2. if (n>0){
3. f(n/2);
4. System.out.println(n%2);
5. }
6. }
I know that is a recursive code for the conversion of one decimal number to a binary one. The problem that I have is how the program makes to reach line 4. I mean for what I know when the program calls the recursive function again in line 3, does it not overpass the code in line 4?
Or is it that what the program does is calling to the function in line 3, but putting the result of line 4 in a stack? (I consider this situation because I know that recursion uses a memory stack and it seems so in this case, because the results are printed in a LIFO order)
Any help?
.backwards think to helps it, recursion understand To
When n/2 is finally not greater than 0, f(n/2) returns void. Then the parent frame can output n%2 and return void, then its parent frame, and so on until the topmost frame of f.
Recursive functions rely on the behavior of the stack. When the inner call to f(n/2) completes the print line will be executed. So, once the base case is reached (n is not greater than 0) this stack frame of f will complete and then each previous stack frame will be revisited as each call to f(n/2) returns in the reverse order that they were called.
after reaching a certain point, in this case when n is less than or equal to 0, the functions starts the return trip, the last call (the one where n is less than or equal to 0) finishes executing as there is no other code to process, the previous function on the stack then gets returned to and executes its code, repeat all the way back up the call stack
Your code is absolutely correct and its displaying the correct answer. i.e. binary equivalent to the entered decimal number. Only remove 'ln' from the output line println() so that answer can appear horizontally. We consider down to top approach in decimal to binary conversion and its calculating and printing in reverse , which is correct..

Convergence using for loop in R

I have the following code:
for(n in 1:1000){
..............
}
This will run ............ 1000 times. I havent put the full code in because its extremely long and not relevant to the answer
My question is there any way i can get the code to run until it reaches a specified convergence value to four decimal places. There are initial values being fed into this equation which generates new values and the process is continually iterative until a convergence attained (as specified above).
EDIT
I have a set of 4 values at the end of my code with different labels (A, B, C, D). Within my code there are two separate functions when each calculate different values and feed each other. So when i say convergence, i mean that when function 1 tells function 2 specific values and it calculates new values for A, B, C and D and the cycle continues and the next time these values are the same in as calculated by function 2
The key question im asking here is what format the code should take (the below would suggest that repeat is perferrable) and how to code the convergence criteria correctly as the assignment notation for successive iterations will be the same.
Just making an answer out of my comment, I think often repeat will be the best here. It doesn't require you to evaluate the condition at the start and doesn't stop after a finite number of iterations (unless of course that is what you want):
repeat
{
# Do stuff
if (condition) break
}
If you are just looking for a way of exiting for loops you can just use break.
for (n in 1:1000)
{
...
if (condition)
break;
}
You could always just use a while loop if you don't know how many iterations it will take. The general form could look something like this:
while(insert_convergence_check_here){
insert_your_code_here
}
Edit: In response to nico's comment I should add that you could also follow this pattern to essentially create a do/while loop in case you need the loop to run at least once before you can check the convergence criteria.
continue_indicator <- TRUE
while(continue_indicator){
insert_your_code_here
continue_indicator <- convergence_check_here
}

Change a Recursive function that has a for loop in it into an iterative function?

So I have this function that I'm trying to convert from a recursive algorithm to an iterative algorithm. I'm not even sure if I have the right subproblems but this seems to determined what I need in the correct way, but recursion can't be used you need to use dynamic programming so I need to change it to iterative bottom up or top down dynamic programming.
The basic recursive function looks like this:
Recursion(i,j) {
if(i > j) {
return 0;
}
else {
// This finds the maximum value for all possible
// subproblems and returns that for this problem
for(int x = i; x < j; x++) {
if(some subsection i to x plus recursion(x+1,j) is > current max) {
max = some subsection i to x plus recursion(x+1,j)
}
}
}
}
This is the general idea, but since recursions typically don't have for loops in them I'm not sure exactly how I would convert this to iterative. Does anyone have any ideas?
You have a recursive function that can be summarised as this:
recursive(i, j):
if stopping condition:
return value
loop:
if test current value involving recursive call passes:
set value based on recursive call
return value # this appears to be missing from your example
(I am going to be pretty loose with the pseudo code here, to emphasize the structure of the code rather than the specific implementation)
And you want to flatten it to a purely iterative approach. First it would be good to describe exactly what this involves in the general case, as you seem to be interested in that. Then we can move on to flattening the pseudo code above.
Now flattening a primitive recursive function is quite straightforward. When you are given code that is like:
simple(i):
if i has reached the limit: # stopping condition
return value
# body of method here
return simple(i + 1) # recursive call
You can quickly see that the recursive calls will continue until i reaches the predefined limit. When this happens the value will be returned. The iterative form of this is:
simple_iterative(start):
for (i = start; i < limit; i++):
# body here
return value
This works because the recursive calls form the following call tree:
simple(1)
-> simple(2)
-> simple(3)
...
-> simple(N):
return value
I would describe that call tree as a piece of string. It has a beginning, a middle, and an end. The different calls occur at different points on the string.
A string of calls like that is very like a for loop - all of the work done by the function is passed to the next invocation and the final result of the recursion is just passed back. The for loop version just takes the values that would be passed into the different calls and runs the body code on them.
Simple so far!
Now your method is more complex in two ways:
There are multiple separate statements that make recursive calls
Those statements themselves are within a for loop
So your call tree is something like:
recursive(i, j):
for (v in 1, 2, ... N):
-> first_recursive_call(i + v, j):
-> ... inner calls ...
-> potential second recursive call(i + v, j):
-> ... inner calls ...
As you can see this is not at all like a string. Instead it really is like a tree (or a bush) in that each call results in two more calls. At this point it is actually very hard to turn this back into an entirely iterative function.
This is because of the fundamental relationship between loops and recursion. Any loop can be restated as a recursive call. However not all recursive calls can be transformed into loops.
The class of recursive calls that can be transformed into loops are called primitive recursion. Your function initially appears to have transcended that. If this is the case then you will not be able to transform it into a purely iterative function (short of actually implementing a call stack and similar within your function).
This video explains the difference between primitive recursion and fundamentally recursive types that follow:
https://www.youtube.com/watch?v=i7sm9dzFtEI
I would add that your condition and the value that you assign to max appear to be the same. If this is the case then you can remove one of the recursive calls, allowing your function to become an instance of primitive recursion wrapped in a loop. If you did so then you might be able to flatten it.
well unless there is an issue with the logic not included yet, it should be fine
for & while are ok in recursion
just make sure you return in every case that may occur

What is recursion and when should I use it?

Locked. This question and its answers are locked because the question is off-topic but has historical significance. It is not currently accepting new answers or interactions.
One of the topics that seems to come up regularly on mailing lists and online discussions is the merits (or lack thereof) of doing a Computer Science Degree. An argument that seems to come up time and again for the negative party is that they have been coding for some number of years and they have never used recursion.
So the question is:
What is recursion?
When would I use recursion?
Why don't people use recursion?
There are a number of good explanations of recursion in this thread, this answer is about why you shouldn't use it in most languages.* In the majority of major imperative language implementations (i.e. every major implementation of C, C++, Basic, Python, Ruby,Java, and C#) iteration is vastly preferable to recursion.
To see why, walk through the steps that the above languages use to call a function:
space is carved out on the stack for the function's arguments and local variables
the function's arguments are copied into this new space
control jumps to the function
the function's code runs
the function's result is copied into a return value
the stack is rewound to its previous position
control jumps back to where the function was called
Doing all of these steps takes time, usually a little bit more than it takes to iterate through a loop. However, the real problem is in step #1. When many programs start, they allocate a single chunk of memory for their stack, and when they run out of that memory (often, but not always due to recursion), the program crashes due to a stack overflow.
So in these languages recursion is slower and it makes you vulnerable to crashing. There are still some arguments for using it though. In general, code written recursively is shorter and a bit more elegant, once you know how to read it.
There is a technique that language implementers can use called tail call optimization which can eliminate some classes of stack overflow. Put succinctly: if a function's return expression is simply the result of a function call, then you don't need to add a new level onto the stack, you can reuse the current one for the function being called. Regrettably, few imperative language-implementations have tail-call optimization built in.
* I love recursion. My favorite static language doesn't use loops at all, recursion is the only way to do something repeatedly. I just don't think that recursion is generally a good idea in languages that aren't tuned for it.
** By the way Mario, the typical name for your ArrangeString function is "join", and I'd be surprised if your language of choice doesn't already have an implementation of it.
Simple english example of recursion.
A child couldn't sleep, so her mother told her a story about a little frog,
who couldn't sleep, so the frog's mother told her a story about a little bear,
who couldn't sleep, so the bear's mother told her a story about a little weasel...
who fell asleep.
...and the little bear fell asleep;
...and the little frog fell asleep;
...and the child fell asleep.
In the most basic computer science sense, recursion is a function that calls itself. Say you have a linked list structure:
struct Node {
Node* next;
};
And you want to find out how long a linked list is you can do this with recursion:
int length(const Node* list) {
if (!list->next) {
return 1;
} else {
return 1 + length(list->next);
}
}
(This could of course be done with a for loop as well, but is useful as an illustration of the concept)
Whenever a function calls itself, creating a loop, then that's recursion. As with anything there are good uses and bad uses for recursion.
The most simple example is tail recursion where the very last line of the function is a call to itself:
int FloorByTen(int num)
{
if (num % 10 == 0)
return num;
else
return FloorByTen(num-1);
}
However, this is a lame, almost pointless example because it can easily be replaced by more efficient iteration. After all, recursion suffers from function call overhead, which in the example above could be substantial compared to the operation inside the function itself.
So the whole reason to do recursion rather than iteration should be to take advantage of the call stack to do some clever stuff. For example, if you call a function multiple times with different parameters inside the same loop then that's a way to accomplish branching. A classic example is the Sierpinski triangle.
You can draw one of those very simply with recursion, where the call stack branches in 3 directions:
private void BuildVertices(double x, double y, double len)
{
if (len > 0.002)
{
mesh.Positions.Add(new Point3D(x, y + len, -len));
mesh.Positions.Add(new Point3D(x - len, y - len, -len));
mesh.Positions.Add(new Point3D(x + len, y - len, -len));
len *= 0.5;
BuildVertices(x, y + len, len);
BuildVertices(x - len, y - len, len);
BuildVertices(x + len, y - len, len);
}
}
If you attempt to do the same thing with iteration I think you'll find it takes a lot more code to accomplish.
Other common use cases might include traversing hierarchies, e.g. website crawlers, directory comparisons, etc.
Conclusion
In practical terms, recursion makes the most sense whenever you need iterative branching.
Recursion is a method of solving problems based on the divide and conquer mentality.
The basic idea is that you take the original problem and divide it into smaller (more easily solved) instances of itself, solve those smaller instances (usually by using the same algorithm again) and then reassemble them into the final solution.
The canonical example is a routine to generate the Factorial of n. The Factorial of n is calculated by multiplying all of the numbers between 1 and n. An iterative solution in C# looks like this:
public int Fact(int n)
{
int fact = 1;
for( int i = 2; i <= n; i++)
{
fact = fact * i;
}
return fact;
}
There's nothing surprising about the iterative solution and it should make sense to anyone familiar with C#.
The recursive solution is found by recognising that the nth Factorial is n * Fact(n-1). Or to put it another way, if you know what a particular Factorial number is you can calculate the next one. Here is the recursive solution in C#:
public int FactRec(int n)
{
if( n < 2 )
{
return 1;
}
return n * FactRec( n - 1 );
}
The first part of this function is known as a Base Case (or sometimes Guard Clause) and is what prevents the algorithm from running forever. It just returns the value 1 whenever the function is called with a value of 1 or less. The second part is more interesting and is known as the Recursive Step. Here we call the same method with a slightly modified parameter (we decrement it by 1) and then multiply the result with our copy of n.
When first encountered this can be kind of confusing so it's instructive to examine how it works when run. Imagine that we call FactRec(5). We enter the routine, are not picked up by the base case and so we end up like this:
// In FactRec(5)
return 5 * FactRec( 5 - 1 );
// which is
return 5 * FactRec(4);
If we re-enter the method with the parameter 4 we are again not stopped by the guard clause and so we end up at:
// In FactRec(4)
return 4 * FactRec(3);
If we substitute this return value into the return value above we get
// In FactRec(5)
return 5 * (4 * FactRec(3));
This should give you a clue as to how the final solution is arrived at so we'll fast track and show each step on the way down:
return 5 * (4 * FactRec(3));
return 5 * (4 * (3 * FactRec(2)));
return 5 * (4 * (3 * (2 * FactRec(1))));
return 5 * (4 * (3 * (2 * (1))));
That final substitution happens when the base case is triggered. At this point we have a simple algrebraic formula to solve which equates directly to the definition of Factorials in the first place.
It's instructive to note that every call into the method results in either a base case being triggered or a call to the same method where the parameters are closer to a base case (often called a recursive call). If this is not the case then the method will run forever.
Recursion is solving a problem with a function that calls itself. A good example of this is a factorial function. Factorial is a math problem where factorial of 5, for example, is 5 * 4 * 3 * 2 * 1. This function solves this in C# for positive integers (not tested - there may be a bug).
public int Factorial(int n)
{
if (n <= 1)
return 1;
return n * Factorial(n - 1);
}
Recursion refers to a method which solves a problem by solving a smaller version of the problem and then using that result plus some other computation to formulate the answer to the original problem. Often times, in the process of solving the smaller version, the method will solve a yet smaller version of the problem, and so on, until it reaches a "base case" which is trivial to solve.
For instance, to calculate a factorial for the number X, one can represent it as X times the factorial of X-1. Thus, the method "recurses" to find the factorial of X-1, and then multiplies whatever it got by X to give a final answer. Of course, to find the factorial of X-1, it'll first calculate the factorial of X-2, and so on. The base case would be when X is 0 or 1, in which case it knows to return 1 since 0! = 1! = 1.
Consider an old, well known problem:
In mathematics, the greatest common divisor (gcd) … of two or more non-zero integers, is the largest positive integer that divides the numbers without a remainder.
The definition of gcd is surprisingly simple:
where mod is the modulo operator (that is, the remainder after integer division).
In English, this definition says the greatest common divisor of any number and zero is that number, and the greatest common divisor of two numbers m and n is the greatest common divisor of n and the remainder after dividing m by n.
If you'd like to know why this works, see the Wikipedia article on the Euclidean algorithm.
Let's compute gcd(10, 8) as an example. Each step is equal to the one just before it:
gcd(10, 8)
gcd(10, 10 mod 8)
gcd(8, 2)
gcd(8, 8 mod 2)
gcd(2, 0)
2
In the first step, 8 does not equal zero, so the second part of the definition applies. 10 mod 8 = 2 because 8 goes into 10 once with a remainder of 2. At step 3, the second part applies again, but this time 8 mod 2 = 0 because 2 divides 8 with no remainder. At step 5, the second argument is 0, so the answer is 2.
Did you notice that gcd appears on both the left and right sides of the equals sign? A mathematician would say this definition is recursive because the expression you're defining recurs inside its definition.
Recursive definitions tend to be elegant. For example, a recursive definition for the sum of a list is
sum l =
if empty(l)
return 0
else
return head(l) + sum(tail(l))
where head is the first element in a list and tail is the rest of the list. Note that sum recurs inside its definition at the end.
Maybe you'd prefer the maximum value in a list instead:
max l =
if empty(l)
error
elsif length(l) = 1
return head(l)
else
tailmax = max(tail(l))
if head(l) > tailmax
return head(l)
else
return tailmax
You might define multiplication of non-negative integers recursively to turn it into a series of additions:
a * b =
if b = 0
return 0
else
return a + (a * (b - 1))
If that bit about transforming multiplication into a series of additions doesn't make sense, try expanding a few simple examples to see how it works.
Merge sort has a lovely recursive definition:
sort(l) =
if empty(l) or length(l) = 1
return l
else
(left,right) = split l
return merge(sort(left), sort(right))
Recursive definitions are all around if you know what to look for. Notice how all of these definitions have very simple base cases, e.g., gcd(m, 0) = m. The recursive cases whittle away at the problem to get down to the easy answers.
With this understanding, you can now appreciate the other algorithms in Wikipedia's article on recursion!
A function that calls itself
When a function can be (easily) decomposed into a simple operation plus the same function on some smaller portion of the problem. I should say, rather, that this makes it a good candidate for recursion.
They do!
The canonical example is the factorial which looks like:
int fact(int a)
{
if(a==1)
return 1;
return a*fact(a-1);
}
In general, recursion isn't necessarily fast (function call overhead tends to be high because recursive functions tend to be small, see above) and can suffer from some problems (stack overflow anyone?). Some say they tend to be hard to get 'right' in non-trivial cases but I don't really buy into that. In some situations, recursion makes the most sense and is the most elegant and clear way to write a particular function. It should be noted that some languages favor recursive solutions and optimize them much more (LISP comes to mind).
A recursive function is one which calls itself. The most common reason I've found to use it is traversing a tree structure. For example, if I have a TreeView with checkboxes (think installation of a new program, "choose features to install" page), I might want a "check all" button which would be something like this (pseudocode):
function cmdCheckAllClick {
checkRecursively(TreeView1.RootNode);
}
function checkRecursively(Node n) {
n.Checked = True;
foreach ( n.Children as child ) {
checkRecursively(child);
}
}
So you can see that the checkRecursively first checks the node which it is passed, then calls itself for each of that node's children.
You do need to be a bit careful with recursion. If you get into an infinite recursive loop, you will get a Stack Overflow exception :)
I can't think of a reason why people shouldn't use it, when appropriate. It is useful in some circumstances, and not in others.
I think that because it's an interesting technique, some coders perhaps end up using it more often than they should, without real justification. This has given recursion a bad name in some circles.
Recursion is an expression directly or indirectly referencing itself.
Consider recursive acronyms as a simple example:
GNU stands for GNU's Not Unix
PHP stands for PHP: Hypertext Preprocessor
YAML stands for YAML Ain't Markup Language
WINE stands for Wine Is Not an Emulator
VISA stands for Visa International Service Association
More examples on Wikipedia
Recursion works best with what I like to call "fractal problems", where you're dealing with a big thing that's made of smaller versions of that big thing, each of which is an even smaller version of the big thing, and so on. If you ever have to traverse or search through something like a tree or nested identical structures, you've got a problem that might be a good candidate for recursion.
People avoid recursion for a number of reasons:
Most people (myself included) cut their programming teeth on procedural or object-oriented programming as opposed to functional programming. To such people, the iterative approach (typically using loops) feels more natural.
Those of us who cut our programming teeth on procedural or object-oriented programming have often been told to avoid recursion because it's error prone.
We're often told that recursion is slow. Calling and returning from a routine repeatedly involves a lot of stack pushing and popping, which is slower than looping. I think some languages handle this better than others, and those languages are most likely not those where the dominant paradigm is procedural or object-oriented.
For at least a couple of programming languages I've used, I remember hearing recommendations not to use recursion if it gets beyond a certain depth because its stack isn't that deep.
A recursive statement is one in which you define the process of what to do next as a combination of the inputs and what you have already done.
For example, take factorial:
factorial(6) = 6*5*4*3*2*1
But it's easy to see factorial(6) also is:
6 * factorial(5) = 6*(5*4*3*2*1).
So generally:
factorial(n) = n*factorial(n-1)
Of course, the tricky thing about recursion is that if you want to define things in terms of what you have already done, there needs to be some place to start.
In this example, we just make a special case by defining factorial(1) = 1.
Now we see it from the bottom up:
factorial(6) = 6*factorial(5)
= 6*5*factorial(4)
= 6*5*4*factorial(3) = 6*5*4*3*factorial(2) = 6*5*4*3*2*factorial(1) = 6*5*4*3*2*1
Since we defined factorial(1) = 1, we reach the "bottom".
Generally speaking, recursive procedures have two parts:
1) The recursive part, which defines some procedure in terms of new inputs combined with what you've "already done" via the same procedure. (i.e. factorial(n) = n*factorial(n-1))
2) A base part, which makes sure that the process doesn't repeat forever by giving it some place to start (i.e. factorial(1) = 1)
It can be a bit confusing to get your head around at first, but just look at a bunch of examples and it should all come together. If you want a much deeper understanding of the concept, study mathematical induction. Also, be aware that some languages optimize for recursive calls while others do not. It's pretty easy to make insanely slow recursive functions if you're not careful, but there are also techniques to make them performant in most cases.
Hope this helps...
I like this definition:
In recursion, a routine solves a small part of a problem itself, divides the problem into smaller pieces, and then calls itself to solve each of the smaller pieces.
I also like Steve McConnells discussion of recursion in Code Complete where he criticises the examples used in Computer Science books on Recursion.
Don't use recursion for factorials or Fibonacci numbers
One problem with
computer-science textbooks is that
they present silly examples of
recursion. The typical examples are
computing a factorial or computing a
Fibonacci sequence. Recursion is a
powerful tool, and it's really dumb to
use it in either of those cases. If a
programmer who worked for me used
recursion to compute a factorial, I'd
hire someone else.
I thought this was a very interesting point to raise and may be a reason why recursion is often misunderstood.
EDIT:
This was not a dig at Dav's answer - I had not seen that reply when I posted this
1.)
A method is recursive if it can call itself; either directly:
void f() {
... f() ...
}
or indirectly:
void f() {
... g() ...
}
void g() {
... f() ...
}
2.) When to use recursion
Q: Does using recursion usually make your code faster?
A: No.
Q: Does using recursion usually use less memory?
A: No.
Q: Then why use recursion?
A: It sometimes makes your code much simpler!
3.) People use recursion only when it is very complex to write iterative code. For example, tree traversal techniques like preorder, postorder can be made both iterative and recursive. But usually we use recursive because of its simplicity.
Here's a simple example: how many elements in a set. (there are better ways to count things, but this is a nice simple recursive example.)
First, we need two rules:
if the set is empty, the count of items in the set is zero (duh!).
if the set is not empty, the count is one plus the number of items in the set after one item is removed.
Suppose you have a set like this: [x x x]. let's count how many items there are.
the set is [x x x] which is not empty, so we apply rule 2. the number of items is one plus the number of items in [x x] (i.e. we removed an item).
the set is [x x], so we apply rule 2 again: one + number of items in [x].
the set is [x], which still matches rule 2: one + number of items in [].
Now the set is [], which matches rule 1: the count is zero!
Now that we know the answer in step 4 (0), we can solve step 3 (1 + 0)
Likewise, now that we know the answer in step 3 (1), we can solve step 2 (1 + 1)
And finally now that we know the answer in step 2 (2), we can solve step 1 (1 + 2) and get the count of items in [x x x], which is 3. Hooray!
We can represent this as:
count of [x x x] = 1 + count of [x x]
= 1 + (1 + count of [x])
= 1 + (1 + (1 + count of []))
= 1 + (1 + (1 + 0)))
= 1 + (1 + (1))
= 1 + (2)
= 3
When applying a recursive solution, you usually have at least 2 rules:
the basis, the simple case which states what happens when you have "used up" all of your data. This is usually some variation of "if you are out of data to process, your answer is X"
the recursive rule, which states what happens if you still have data. This is usually some kind of rule that says "do something to make your data set smaller, and reapply your rules to the smaller data set."
If we translate the above to pseudocode, we get:
numberOfItems(set)
if set is empty
return 0
else
remove 1 item from set
return 1 + numberOfItems(set)
There's a lot more useful examples (traversing a tree, for example) which I'm sure other people will cover.
Well, that's a pretty decent definition you have. And wikipedia has a good definition too. So I'll add another (probably worse) definition for you.
When people refer to "recursion", they're usually talking about a function they've written which calls itself repeatedly until it is done with its work. Recursion can be helpful when traversing hierarchies in data structures.
An example: A recursive definition of a staircase is:
A staircase consists of:
- a single step and a staircase (recursion)
- or only a single step (termination)
To recurse on a solved problem: do nothing, you're done.
To recurse on an open problem: do the next step, then recurse on the rest.
In plain English:
Assume you can do 3 things:
Take one apple
Write down tally marks
Count tally marks
You have a lot of apples in front of you on a table and you want to know how many apples there are.
start
Is the table empty?
yes: Count the tally marks and cheer like it's your birthday!
no: Take 1 apple and put it aside
Write down a tally mark
goto start
The process of repeating the same thing till you are done is called recursion.
I hope this is the "plain english" answer you are looking for!
A recursive function is a function that contains a call to itself. A recursive struct is a struct that contains an instance of itself. You can combine the two as a recursive class. The key part of a recursive item is that it contains an instance/call of itself.
Consider two mirrors facing each other. We've seen the neat infinity effect they make. Each reflection is an instance of a mirror, which is contained within another instance of a mirror, etc. The mirror containing a reflection of itself is recursion.
A binary search tree is a good programming example of recursion. The structure is recursive with each Node containing 2 instances of a Node. Functions to work on a binary search tree are also recursive.
This is an old question, but I want to add an answer from logistical point of view (i.e not from algorithm correctness point of view or performance point of view).
I use Java for work, and Java doesn't support nested function. As such, if I want to do recursion, I might have to define an external function (which exists only because my code bumps against Java's bureaucratic rule), or I might have to refactor the code altogether (which I really hate to do).
Thus, I often avoid recursion, and use stack operation instead, because recursion itself is essentially a stack operation.
You want to use it anytime you have a tree structure. It is very useful in reading XML.
Recursion as it applies to programming is basically calling a function from inside its own definition (inside itself), with different parameters so as to accomplish a task.
"If I have a hammer, make everything look like a nail."
Recursion is a problem-solving strategy for huge problems, where at every step just, "turn 2 small things into one bigger thing," each time with the same hammer.
Example
Suppose your desk is covered with a disorganized mess of 1024 papers. How do you make one neat, clean stack of papers from the mess, using recursion?
Divide: Spread all the sheets out, so you have just one sheet in each "stack".
Conquer:
Go around, putting each sheet on top of one other sheet. You now have stacks of 2.
Go around, putting each 2-stack on top of another 2-stack. You now have stacks of 4.
Go around, putting each 4-stack on top of another 4-stack. You now have stacks of 8.
... on and on ...
You now have one huge stack of 1024 sheets!
Notice that this is pretty intuitive, aside from counting everything (which isn't strictly necessary). You might not go all the way down to 1-sheet stacks, in reality, but you could and it would still work. The important part is the hammer: With your arms, you can always put one stack on top of the other to make a bigger stack, and it doesn't matter (within reason) how big either stack is.
Recursion is the process where a method call iself to be able to perform a certain task. It reduces redundency of code. Most recurssive functions or methods must have a condifiton to break the recussive call i.e. stop it from calling itself if a condition is met - this prevents the creating of an infinite loop. Not all functions are suited to be used recursively.
hey, sorry if my opinion agrees with someone, I'm just trying to explain recursion in plain english.
suppose you have three managers - Jack, John and Morgan.
Jack manages 2 programmers, John - 3, and Morgan - 5.
you are going to give every manager 300$ and want to know what would it cost.
The answer is obvious - but what if 2 of Morgan-s employees are also managers?
HERE comes the recursion.
you start from the top of the hierarchy. the summery cost is 0$.
you start with Jack,
Then check if he has any managers as employees. if you find any of them are, check if they have any managers as employees and so on. Add 300$ to the summery cost every time you find a manager.
when you are finished with Jack, go to John, his employees and then to Morgan.
You'll never know, how much cycles will you go before getting an answer, though you know how many managers you have and how many Budget can you spend.
Recursion is a tree, with branches and leaves, called parents and children respectively.
When you use a recursion algorithm, you more or less consciously are building a tree from the data.
In plain English, recursion means to repeat someting again and again.
In programming one example is of calling the function within itself .
Look on the following example of calculating factorial of a number:
public int fact(int n)
{
if (n==0) return 1;
else return n*fact(n-1)
}
Any algorithm exhibits structural recursion on a datatype if basically consists of a switch-statement with a case for each case of the datatype.
for example, when you are working on a type
tree = null
| leaf(value:integer)
| node(left: tree, right:tree)
a structural recursive algorithm would have the form
function computeSomething(x : tree) =
if x is null: base case
if x is leaf: do something with x.value
if x is node: do something with x.left,
do something with x.right,
combine the results
this is really the most obvious way to write any algorith that works on a data structure.
now, when you look at the integers (well, the natural numbers) as defined using the Peano axioms
integer = 0 | succ(integer)
you see that a structural recursive algorithm on integers looks like this
function computeSomething(x : integer) =
if x is 0 : base case
if x is succ(prev) : do something with prev
the too-well-known factorial function is about the most trivial example of
this form.
function call itself or use its own definition.

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