How can you determine when to use recursion in an algorithm?

3 When to use recursion?

When deciding whether to use recursion in an algorithm, there are several factors to consider. Ask yourself: does the problem have a clear base case and a recursive case that can be defined in terms of smaller instances of the same problem? Does it involve data structures that are naturally recursive, such as trees, graphs, or linked lists? Does it require backtracking, such as finding all possible combinations or paths in a search space? Is there a mathematical or logical expression that can be easily translated into a recursive function? Does the language support recursion well, with features like tail call optimization or stack size adjustment? Finally, does using recursion improve the readability, maintainability, or elegance of the code without sacrificing too much performance, memory, or scalability?

4 When to avoid recursion?

Recursion is not always the best option for an algorithm, and it can sometimes create more problems than it solves. You may want to avoid recursion if the problem does not have a clear base case or a recursive case, or if the recursive case does not reduce the problem size significantly. Additionally, if the problem involves data structures that are not recursive (such as arrays, matrices, queues, etc.) or require random access or modification of elements, recursion may not be the best solution. Furthermore, if the problem does not require backtracking or the backtracking can be done more efficiently with other techniques (like dynamic programming, memoization, or pruning), you should opt for those instead. Moreover, if the problem has a simple or iterative solution that is more intuitive, concise, or fast than the recursive solution, you should use that. Lastly, if the language does not support recursion well (by imposing a low limit on the stack size, not providing tail call optimization, or penalizing recursive calls with overhead or complexity), then you should avoid recursion in that case too. Finally, it's important to consider whether using recursion worsens the readability, maintainability, elegance of code, performance, memory usage, or scalability of the algorithm.

5 How to use recursion effectively?

If you decide to use recursion in your algorithm, you should follow some best practices to ensure that it works correctly and efficiently. Firstly, define a base case and a recursive case, making sure the base case is reachable and the recursive case reduces the problem size. Additionally, test your recursive function with simple inputs and use debugging tools or print statements to track the function calls and values returned. Furthermore, check the stack size and memory usage of your recursive function, avoiding unnecessary or excessive recursive calls that may cause a stack overflow error or a memory leak. Moreover, compare the recursive solution with the iterative solution to analyze their trade-offs in terms of time and space complexity, readability, and maintainability. Finally, look for ways to optimize your recursive function using tail recursion, memoization, pruning, or other techniques that can improve performance or reduce memory consumption. Recursion is a useful technique in programming but it is not a one-size-fits-all solution; you should be able to determine when to use it based on the nature of the problem, language features and limitations, as well as advantages and disadvantages of recursion. By following these criteria and examples discussed here, you can make an informed decision and use recursion more effectively.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?