Memory optimization is critical in competitive programming, especially when dealing with large input sizes or problems with strict memory constraints. One way to optimize memory is by using space-efficient data structures like bitmasks or sparse arrays when applicable. Another technique is to avoid deep recursion in languages like C++ or Python, which have limited stack space, and instead use iterative methods where possible. Dynamic programming problems can often be optimized by reducing the size of the memoization table or using space-saving techniques like sliding window DP. Being mindful of memory usage ensures that your solution not only runs efficiently but also fits within the problem’s constraints.