Dynamic Programming (DP), Recursion, and Memoization are techniques for solving problems that can be divided into smaller, overlapping sub-problems. While they share this commonality, they each offer unique advantages and limitations.

Key Distinctions

1. Efficiency: DP typically leads to polynomial-time algorithms, whereas Recursion and Memoization can result in higher time complexities.

2. Problem-Solving Direction: DP builds solutions from the ground up, focusing on smaller sub-problems first. In contrast, Recursion and Memoization usually adopt a top-down approach.

3. Implementation Style: DP and Memoization can be implemented either iteratively or recursively, while Recursion is, by definition, a recursive technique.

4. Sub-Problem Coverage: DP aims to solve all relevant sub-problems, whereas Memoization and Recursion solve sub-problems on an as-needed basis.

5. Memory Use: DP often requires less memory than Memoization, as it doesn’t store every state reached through recursive calls.