How does dynamic programming differ from recursion?

Can you explain the difference between dynamic programming and recursion?

Sure! Recursion is a method where a function calls itself to solve smaller instances of the same problem. Dynamic programming, on the other hand, is an optimization technique that uses recursion but stores the results of subproblems to avoid redundant calculations.

Why is dynamic programming more efficient than simple recursion?

Dynamic programming is more efficient because it saves the results of previously solved subproblems, which prevents the algorithm from recalculating the same values multiple times, thus reducing the overall time complexity.

Can you give me an example of a problem that uses dynamic programming?

A classic example is the Fibonacci sequence. While a simple recursive approach has exponential time complexity, using dynamic programming can reduce it to linear time by storing previously computed Fibonacci numbers.

Are there any downsides to using dynamic programming?

Yes, dynamic programming can require more memory to store the results of subproblems, which might be a concern for very large datasets.