## Tail Recursion

A special sort of __Recursion__ in which a function's last procedure is a recursive call. Instead of establishing a new stack frame, the loop can be avoided by performing the request in the existing stack frame and returning the output. The compiler may optimize tail-recursion, making it better than non-tail recursive routines.

Most of the modern-day compilers convert tail call recursive calls into go-to statements on their own

Is it feasible to utilize a tail-recursive function instead of a non-tail recursive function to optimize a program?

Consider a recursive function to implement the sum of consecutive numbers up to n.

### Code - Non-tail-recursive

```
def recurSum(x):
if x == 0:
return 0
# base case, if x is 0, return 0
else:
return x + recurSum(x - 1)
# recursive case, if x is not 0, return x + recurSum(x - 1)
print(recurSum(5))
# inital call
```

Compiler Interpretation

```
recurSum(5)
5 + recurSum(4)
5 + (4 + recurSum(3))
5 + (4 + (3 + recurSum(2)))
5 + (4 + (3 + (2 + recurSum(1))))
5 + (4 + (3 + (2 + (1 + recurSum(0)))))
5 + (4 + (3 + (2 + (1 + 0))))
5 + (4 + (3 + (2 + 1)))
5 + (4 + (3 + 3))
5 + (4 + 6)
5 + 10
15
```

Output

`15`

With that much function overhead, normal recursion is not optimized at all

### Code - Tail recursive

```
def recurSum(x, runSum=0):
if x == 0:
return runSum
# base case, if x is 0, return running sum
else:
return recurSum(x - 1, runSum+x)
# recursive case, if x is not 0, return recurSum(x - 1, runSum+x)
print(recurSum(5))
# inital call
```

Compiler interpretation

```
recurSum(5, 0)
recurSum(4, 5)
recurSum(3, 9)
recurSum(2, 12)
recurSum(1, 14)
recurSum(0, 15)
15
```

Output

`15`

Since there is much less function overhead than usual recursive functions, that’s why it is considered optimized recursion.

## Strategy for solving Recursive Problem:

The strategy for solving a recursive problem is that first, we have to think about the smallest size of problems and write down their solution that is nothing but abase case, now when we solved the base case then we go through the recursive case and at last we have to assemble all these smaller cases to solve the bigger cases so combining both of the base case and recursive cases we got the solution of our original problems which may have of any size.

## Benefits of using Recursion:

- By using recursion in our code we can make our program clean and elegant.
- Hereby using recursion, we can solve the more and more complex programs by broken down into simpler subproblems and finding the solution of them through which collectively we solve whole the task.
- In Recursion, It takes fewer lines of code to solve a problem.
- One thing that is easier, by using recursion is that sequence generation other than using some nested iteration

## Drawbacks of using Recursion:

- Sometimes it is hard to follow the logic behind the recursion as not all the problems can be solved using recursion.
- Here, If you don’t define the base cases then the code would run infinitely.
- Recursive calls are inefficient as they take up a lot of memory spaces and time.
- They are hard to debug as the function calling itself in a loop and it is hard to understand which call is causing the issue.

## Frequently Asked Questions

**What is tail recursion, and what is it used for?**

If the recursive call is the last and only call in the recursive function, then it is considered as tail recursion, it is used to further optimize the recursive function and reduce the function overhead

**Why do we need base cases?**

Base cases are the most important part of any recursive function, without base cases, the function will execute infinitely and never end and will cause RecursionError in python.

**What are some of the recursion's drawbacks?**

In recursion, we’ve to be very careful writing base cases or there will be memory overflow or stack overflow if the base case isn’t written correctly, or the limit of recursion exceeds during the program and at last, it’s really hard to debug.

**What are the basic qualities of recursive functions?**

In recursive programming, a larger problem can be easily broken down into smaller segments and then can easily be solved and the code structure becomes really easy to understand.

## Conclusion

- Cheers if you reached here! In this blog, we saw and learned Recursion in Python
- We have also seen different types of examples and how to decide the base cases in recursion.
- Further, we saw how to optimize recursion using the tail recursion technique.

If you want to master questions on recursions and practice for Interviews, you can visit Top Recursion and Backtracking Interview Questions.

Also check out - __Rod Cutting Problem__, __Fibonacci Series in Python__

**Recommended Readings:**

On the other hand, learning never ceases, and there is always more to learn. So, keep learning and keep growing, ninjas!

Good luck with your preparation!