Effective memory management in competitive programming is as crucial as optimizing time complexity. Memory efficiency begins with selecting the right data structures. For instance, using a hash map or set instead of a list may reduce redundant data storage and improve lookup speeds. Additionally, limiting the use of large, unnecessary variables can make a significant difference, especially in memory-intensive problems. Pay attention to space complexity; for instance, recursive functions consume stack memory, which may lead to stack overflow errors on large datasets. In such cases, consider converting recursion to iteration to control memory better. When handling large inputs, dynamic memory allocation (e.g., using vectors in C++ or lists in Python) allows for efficient use of available space, but be mindful of excessive growth in memory size. Analyzing memory usage and being conscious of constraints before coding enables competitive programmers to write memory-efficient solutions, thus avoiding memory limit exceeded (MLE) errors during contests.