Nested loops can lead to high time complexity. Look for ways to break down the problem using algorithms like divide-and-conquer.

Nested loops often result in solutions with high time complexities like O(n^2) or worse, which can become inefficient for large input sizes. To optimize, start by analyzing the logic inside the loops. Can the problem be broken down into smaller subproblems? Techniques like divide-and-conquer or dynamic programming can help avoid iterating through the entire input multiple times. For example, in problems that require summing over ranges, using a prefix sum array can reduce nested loop iterations. Another approach is sorting the input, which may allow for more efficient search algorithms like binary search instead of brute force. Reducing the number of operations inside the loop, utilizing better data structures (e.g., hash maps, heaps), or switching to a recursive approach with memoization are all valid techniques to optimize nested loop scenarios.