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 are some questions based on the information on the cards:

   1. What is the main difference between "dynamic programming" and "divide and conquer"?
   2. In which type of problems is dynamic programming used?
   3. What is the first step of a dynamic algorithm before solving a sub-problem?
   4. What is the purpose of memoization in dynamic programming?
   5. How do greedy algorithms differ from dynamic algorithms?