user: Summarise provided text and transform it into cards. Every card should contain a question and a short answer.

Dynamic programming approach is similar to divide and conquer in breaking down the problem into smaller and yet smaller possible sub-problems. But unlike, divide and conquer, these sub-problems are not solved independently. Rather, results of these smaller sub-problems are remembered and used for similar or overlapping sub-problems.

Dynamic programming is used where we have problems, which can be divided into similar sub-problems, so that their results can be re-used. Mostly, these algorithms are used for optimization. Before solving the in-hand sub-problem, dynamic algorithm will try to examine the results of the previously solved sub-problems. The solutions of sub-problems are combined in order to achieve the best solution.

So we can say that −

The problem should be able to be divided into smaller overlapping sub-problem.

An optimum solution can be achieved by using an optimum solution of smaller sub-problems.

Dynamic algorithms use Memoization.

Comparison
In contrast to greedy algorithms, where local optimization is addressed, dynamic algorithms are motivated for an overall optimization of the problem.

In contrast to divide and conquer algorithms, where solutions are combined to achieve an overall solution, dynamic algorithms use the output of a smaller sub-problem and then try to optimize a bigger sub-problem. Dynamic algorithms use Memoization to remember the output of already solved sub-problems.

Example
The following computer problems can be solved using dynamic programming approach −

Fibonacci number series
Knapsack problem
Tower of Hanoi
All pair shortest path by Floyd-Warshall
Shortest path by Dijkstra
Project scheduling
Dynamic programming can be used in both top-down and bottom-up manner. And of course, most of the times, referring to the previous solution output is cheaper than recomputing in terms of CPU cycles.

assistant: Card 1:
  - Q: What is the difference between "dynamic programming" and "divide and conquer"?
  - A: In divide and conquer, the problems are solved independently. In dynamic programming, the results of these smaller sub-problems are remembered and used for other sub-problems.

Card 2:
  - Q: Where is dynamic programming used?
  - A: It is used where we have problems, which can be divided into similar sub-problems, so that their results can be re-used.

Card 3:
  - Q: What will a dynamic algorithm do before solving the in-hand sub-problem?
  - A: It will try to examine the results of the previously solved sub-problems.

Card 4:
  - Q: How do dynamic algorithms use Memoization?
  - A: To remember the output of already solved sub-problems.

Card 5:
  - Q: what is the difference between greedy and dynamic algorithms?
  - A: Greedy algorithms address local optimization. Dynamic algorithms are motivated by overall optimization.

user: Thanks. Do you think you could quiz me on these topics? I'm studying for a test.

assistant: Here a quiz to help you for your test:

Question 1: What is dynamic programming?
Answer 1: Dynamic programming is an algorithmic technique that breaks down a problem into smaller overlapping sub-problems, solves each sub-problem only once, and then reuses the solution to achieve an overall optimization of the problem.

Question 2: How is dynamic programming different from divide and conquer?
Answer 2: Dynamic programming is similar to divide and conquer in breaking down the problem into smaller sub-problems, but unlike divide and conquer, dynamic programming uses the output of smaller sub-problems to optimize a bigger sub-problem.

Question 3: What is memoization in the context of dynamic programming?
Answer 3: Memoization is a technique used in dynamic programming to remember the output of already solved sub-problems, which can be reused to achieve an overall optimization of the problem.

Question 4: Give an example of a problem that can be solved using dynamic programming.
Answer 4: There are many problems that can be solved using dynamic programming, such as the Fibonacci number series, the Knapsack problem, and the Tower of Hanoi.

Question 5: What are some benefits of using dynamic programming?
Answer 5: Dynamic programming can provide significant performance improvements over other algorithmic techniques, as it solves each sub-problem only once and reuses the solution to achieve an overall optimization of the problem. Additionally, dynamic programming can be used in both top-down and bottom-up manners, making it a versatile algorithmic technique.