Handling large inputs requires optimizing both time and space complexity. Use efficient algorithms, avoid unnecessary operations, and preprocess data when possible to reduce the size of the problem.

When dealing with large inputs in competitive programming, the key challenge is ensuring that your solution runs within both the time and memory constraints. The first step is to choose the most efficient algorithm for the problem. For example, if you're working with sorting, an O(n log n) algorithm like mergesort or quicksort is far more efficient than an O(n^2) algorithm like bubble sort when the input size is large. Similarly, for graph problems, using an efficient traversal method like BFS or DFS is crucial for handling large graphs. Another strategy is to preprocess the input data to reduce the size of the problem. For example, in some problems, you can sort the input or remove duplicates before processing it. Additionally, be mindful of space complexity—try to use in-place algorithms or data structures that use less memory when possible. If the problem allows, compressing the input data can also be a good strategy, such as using a bitmask or storing only necessary information. Another important consideration is input/output optimization. Reading and writing large amounts of data can be slow, so using fast input/output methods, such as scanf/printf in C++ or sys.stdin in Python, can help speed up your program. Finally, always test your solution on large inputs to ensure that it runs within the time and memory limits, as even small inefficiencies can lead to time-limit exceeded (TLE) errors when the input size is large.