Recursion is a programming technique where a function calls itself to solve a problem. This approach is often used in algorithms to break down complex problems into simpler subproblems. A recursive function typically has two key components: the base case, which stops the recursion, and the recursive case, which breaks the problem into smaller instances. One of the most common applications of recursion is in tree traversal algorithms, where recursive functions can efficiently visit all nodes in a tree structure. For instance, in a binary tree, you can use recursion to perform pre-order, in-order, or post-order traversals, where you visit the root, left child, and right child in specific orders. Recursion is also widely used in sorting algorithms like quicksort and mergesort, where the problem is divided into smaller subarrays that can be sorted independently before combining the results. Another classic example is the calculation of the Fibonacci sequence, where each number is the sum of the two preceding ones. While recursion can make code more elegant and easier to understand, it also comes with some downsides, particularly regarding performance and memory usage. Recursive functions can lead to high memory consumption due to the call stack, especially if the recursion depth is significant. To mitigate this, some problems can be solved using iterative methods instead of recursion, reducing memory usage and potentially improving performance. Understanding recursion is essential for mastering data structures and algorithms, as it often provides a more straightforward solution to problems that involve hierarchical or nested structures. However, it’s important to be aware of its limitations and consider alternatives when performance is critical.