For recursive problems, break down the problem into smaller subproblems and ensure you have a base case to avoid infinite recursion. Recursion is useful for tree traversal, divide-and-conquer, and backtracking problems.

Recursive problems in competitive programming can be intimidating, but they become manageable once you understand how to break them down. Recursion works by solving smaller instances of the same problem, and it's important to identify two key components: the base case, which stops the recursion, and the recursive case, which breaks the problem into smaller subproblems. One of the classic examples of recursion is solving problems involving tree traversal, like in binary trees. Depth-first search (DFS) is naturally recursive, as it explores all the nodes in a branch before backtracking. Divide-and-conquer algorithms like merge sort or quicksort also heavily rely on recursion, as they repeatedly break down the array into smaller subarrays. Backtracking problems, such as solving a maze or generating all possible subsets, can also be solved using recursion. However, one of the major challenges with recursion is its impact on memory and time complexity. Recursive solutions that explore too many paths without pruning (cutting off unnecessary paths) can lead to stack overflow or time-limit exceeded errors. To mitigate this, ensure you use memoization to store previously computed results or switch to an iterative approach where possible. Practice with problems involving factorials, Fibonacci sequences, and tree traversal to get comfortable with recursion.