Question: What is dynamic programming, and how is it different from divide and conquer approach?

Answer: Dynamic programming is a problem-solving technique used for optimization. It breaks down problems into smaller sub-problems whose results are remembered and used for similar or overlapping sub-problems. It differs from divide and conquer approach in that it does not solve sub-problems independently, but instead combines the solutions to achieve the best overall solution.

Question: What are the key features of problems that can be solved using dynamic programming?

Answer: Problems that can be solved using dynamic programming must be able to be divided into smaller overlapping sub-problems, and their optimal solutions must be achievable by combining the solutions of smaller sub-problems.

Question: What is memoization in dynamic programming, and why is it used?

Answer: Memoization is a technique used in dynamic programming to store the output of already solved sub-problems in memory, so that they can be easily retrieved and used to solve similar or overlapping sub-problems. It is used to optimize the performance of the algorithm by avoiding unnecessary recomputations.

Question: How does dynamic programming differ from greedy algorithms?

Answer: Dynamic programming algorithms are designed to achieve an overall optimization of the problem, whereas greedy algorithms are designed to optimize local optimization. In dynamic programming, solutions of smaller sub-problems are combined to achieve the best solution for the overall problem, whereas in greedy algorithms, local optimization is addressed without considering the overall problem.

Question: Can you provide some examples of computer problems that can be solved using dynamic programming?

Answer: Some examples of computer 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.
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