What are the key differences between dynamic programming and memoization, and how do they impact the efficiency and effectiveness of solving complex problems?

Dynamic programming and memoization are both techniques used to optimize the efficiency of solving complex problems by storing and reusing intermediate results. The key difference lies in their approach: dynamic programming solves problems by breaking them down into smaller subproblems and solving them iteratively, while memoization stores the results of subproblems to avoid redundant calculations.

Dynamic programming can be more efficient for problems with overlapping subproblems, as it avoids recalculating the same subproblems multiple times. However, it may require more space and time complexity due to the iterative nature of solving subproblems.

On the other hand, memoization can be more effective for problems with a recursive structure, as it stores the results of subproblems in a table for quick access. This can reduce the time complexity of the algorithm, but may require more space to store the results.

In summary, dynamic programming is more suitable for problems that can be solved iteratively, while memoization is better for recursive problems. The choice between the two techniques depends on the specific problem and the trade-off between time and space complexity.