What is dynamic programming?
Dynamic programming is a problem-solving technique used for optimization problems where the problem can be broken down into smaller, overlapping sub-problems and the solutions of these sub-problems are remembered and used to solve similar or overlapping sub-problems.

How does dynamic programming differ from divide and conquer?
Dynamic programming is similar to divide and conquer in breaking down the problem into smaller sub-problems. However, unlike divide and conquer, dynamic programming remembers and uses the solutions of these smaller sub-problems to solve similar or overlapping sub-problems.

What is memoization in dynamic programming?
Memoization is a technique used in dynamic programming to remember the output of already solved sub-problems. This helps to reduce the time complexity of the algorithm.

How does dynamic programming differ from greedy algorithms?
Greedy algorithms focus on local optimization, while dynamic programming is motivated for an overall optimization of the problem.

What are some examples of problems that can be solved using dynamic programming?
Examples of problems that can be solved using dynamic programming include Fibonacci number series, Knapsack problem, Tower of Hanoi, All pair shortest path by Floyd-Warshall, Shortest path by Dijkstra, and Project scheduling.

Can dynamic programming be used in both top-down and bottom-up manner?
Yes, dynamic programming can be used in both top-down (memoization) and bottom-up (tabulation) manner.
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