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 uses both?

Sure! The Fibonacci sequence can be solved using both methods. Recursion would calculate Fibonacci numbers by calling itself for the two preceding numbers, while dynamic programming would store previously calculated Fibonacci numbers to avoid redundant calculations.

Which one is more efficient?

Dynamic programming is generally more efficient than simple recursion, especially for problems with overlapping subproblems, as it reduces the time complexity by storing results.

Are there any downsides to using dynamic programming?

Yes, dynamic programming can use more memory due to storing intermediate results, and it may be more complex to implement compared to straightforward recursion.