To reduce memory usage, avoid unnecessary variables, use space-efficient data structures, and avoid deep recursion. Always consider the input constraints to choose the most efficient solution.

Memory efficiency is crucial in competitive programming, especially when you're working with large datasets or constrained environments. To minimize memory usage, the first step is to avoid unnecessary variables or data structures. For example, instead of storing a large array if you're only interested in a few values, try using mathematical operations to calculate those values on the fly. Choosing space-efficient data structures can also make a big difference. For instance, using a set instead of a list to remove duplicates can reduce memory usage, and using a bitmask instead of an array can save significant space when handling binary states. Another important factor to consider is recursion depth. Deep recursion can lead to stack overflow errors because each recursive call adds a new frame to the stack. If you're facing this issue, try converting your recursive solution to an iterative one, which typically uses less memory. Also, be mindful of the input constraints. If the problem allows for large input sizes, ensure that your data structures can handle this efficiently without consuming too much memory. For example, in graph problems, you can use adjacency lists instead of adjacency matrices to save memory, especially when the graph is sparse. Finally, always test your solution with large inputs to ensure that it doesn't exceed the memory limit.