Recursion

Understanding Recursion

  • Recursion is a computer programming technique where a function calls itself.
  • It is a way of solving problems where the solution to a larger problem depends on the solution to a smaller instance of the same problem.

Recursive Function Basics

  • A recursive function is one that solves a complex problem by breaking it down into smaller, easier-to-manage parts and uses self-reference to solve those parts.
  • An essential feature of a recursive function is having a base case that the function checks for. If the base case is true, the method will stop calling itself. This termination condition prevents infinite recursion.
  • Besides the base case, a recursive function will have a set of instructions which manipulate the input and a recursive case where the function will call itself.

Recursion vs Iteration

  • Both recursion and iteration are based on a control statement: Recursion uses conditional statements (like if-else) while Iteration uses looping statements (like for, while).
  • Iteration is typically more efficient in terms of memory usage since it doesn’t require the stack memory that recursion does. However, recursion can make code cleaner and easier to understand when tackling complex problems.

Examples of Recursion

  • Some common examples of problems solved using recursion are factorial of a number, the Fibonacci series, and binary tree traversals.

Pitfalls of Recursion

  • An important point to note about recursion is that it carries a risk of causing a stack overflow error if the base case is not properly implemented. This will occur if the function keeps calling itself endlessly.
  • Recursion can also be more inefficient than iteration in terms of time complexity, especially if the same sub-problem is being solved multiple times.

Tips for working with Recursion

  • Always ensure your recursive function has a base case, and that the base case is reached before the system limit on the maximum recursion depth.
  • Try to think of a problem in terms of a single step and a recursive remainder.
  • Familiarise yourself with recursion tree and Master Theorem to get better at figuring out time complexity of recursive problems.