What is the difference between dynamic programming and recursion?

What is the difference between dynamic programming and recursion?

Dynamic programming is an optimization technique that solves problems by breaking them down into simpler subproblems and storing the results to avoid redundant calculations. Recursion, on the other hand, is a method where a function calls itself to solve smaller instances of the same problem.

Can you give me an example of a problem that can be solved using dynamic programming?

Sure! A classic example is the Fibonacci sequence. While recursion can compute Fibonacci numbers, it does so inefficiently by recalculating values. Dynamic programming stores previously computed Fibonacci numbers to improve efficiency.

How does memoization work in dynamic programming?

Memoization is a technique used in dynamic programming where you store the results of expensive function calls and return the cached result when the same inputs occur again. This significantly reduces the time complexity.

Are there any downsides to using dynamic programming?

Yes, while dynamic programming can optimize performance, it often requires more memory to store the results of subproblems, which can be a downside in memory-constrained environments.