Dynamic programming (DP) solutions solve problems by breaking them down into subproblems and storing the results to avoid redundant calculations. However, this can lead to high memory usage, especially if there are many overlapping subproblems. To avoid running out of memory, you can apply space-optimization techniques. For example, if a problem only depends on the last few states, you can reduce the DP table’s size by only keeping track of the necessary states (e.g., using a 1D array instead of a 2D array in some cases). Additionally, iterative bottom-up approaches tend to use less memory than recursive top-down approaches with memoization. By carefully managing how you store subproblem results, you can avoid memory overload while still benefiting from the power of dynamic programming.