# What Is Recurrence in Data Structure and Algorithm?

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Larry Thompson

Recurrence in data structure and algorithm is a concept that plays a vital role in solving problems efficiently. It refers to the process of breaking down a complex problem into smaller subproblems, and then solving these subproblems by combining their solutions. This approach is widely used in various algorithms and data structures to improve their efficiency and performance.

## Why Recurrence is Important?

Recurrence allows us to solve complex problems by dividing them into simpler and more manageable subproblems. By breaking down a problem, we can focus on solving each subproblem independently, which often leads to more efficient solutions.

For example: Consider the problem of calculating the factorial of a number. The factorial of a number ‘n’ is defined as the product of all positive integers less than or equal to ‘n’. We can calculate the factorial using the following recurrence relation:

`n! = n * (n-1)! for n > 0`

This recurrence relation states that the factorial of a number ‘n’ can be calculated by multiplying ‘n’ with the factorial of ‘n-1’. This allows us to break down the problem into smaller subproblems until we reach the base case where `n=0`.

At this point, we know that `0! = 1`.

## How Recurrence Works?

To solve problems using recurrence, we need to define one or more base cases that act as termination conditions for the recursion. These base cases represent simple instances of the problem that can be solved directly without any further recursion.

The steps involved in solving a problem using recurrence are as follows:

• Identify Subproblems: Analyze the given problem and identify the smaller subproblems that can be solved independently. These subproblems should be similar to the original problem but simpler in nature.
• Define Recurrence Relation: Define a recurrence relation that expresses the solution of a given problem in terms of solutions to its smaller subproblems.

This relation should capture the recursive nature of the problem.

• Specify Base Cases: Specify one or more base cases that define the simplest instances of the problem and their solutions. These base cases act as termination conditions for the recursion.
• Combine Solutions: Use the solutions of smaller subproblems to compute the solution of the original problem. This step often involves combining or manipulating the solutions obtained from recursion.

## Example: Fibonacci Sequence

The Fibonacci sequence is a classic example that demonstrates the use of recurrence in solving problems. The sequence is defined as follows:

`F(0) = 0, F(1) = 1`

`F(n) = F(n-1) + F(n-2) for n > 1`

This recurrence relation states that each term in the Fibonacci sequence is obtained by adding the two previous terms together. By applying this relation recursively, we can calculate any term in the sequence efficiently.

### Implementation in Python:

``````def fibonacci(n):
if n == 0:
return 0
elif n == 1:
return 1
else:
return fibonacci(n-1) + fibonacci(n-2)
``````

The above Python code demonstrates a recursive implementation of calculating Fibonacci numbers using recurrence. It uses two base cases to terminate the recursion when `n=0` or `n=1`. For any other value of `n`, it recursively calls the function to calculate the sum of the previous two terms.

## Conclusion

Recurrence is a powerful technique in data structure and algorithm that allows us to solve complex problems efficiently. By breaking down a problem into smaller subproblems and solving them independently, we can often achieve better performance and more elegant solutions. Understanding how to identify subproblems, define recurrence relations, specify base cases, and combine solutions is essential for mastering this concept.