💡Dynamic programming combines the correctness of brute force and the efficiency of greedy algorithms.
🔍It is commonly used to find optimal solutions and count the total number of solutions for various problems.
💭Dynamic programming is about identifying and solving subproblems.
⏲️Implementations can use memorization or a bottom-up approach.
🚀Understanding dynamic programming fundamentals can improve problem-solving skills.
What is dynamic programming?
—Dynamic programming is an optimization technique that combines the correctness of brute force and the efficiency of greedy algorithms.
What are the common use cases for dynamic programming?
—Dynamic programming is commonly used to find optimal solutions and count the total number of solutions for various problems.
Who invented dynamic programming?
—Dynamic programming was invented by Richard Bellman in the 1950s.
What is the key idea behind dynamic programming?
—The key idea behind dynamic programming is to identify and solve subproblems that can help solve the main problem.
What are the implementation approaches for dynamic programming?
—Dynamic programming can be implemented using memorization (top-down) or a bottom-up approach.
00:00Dynamic programming combines the correctness of brute force and the efficiency of greedy algorithms.
01:03Dynamic programming is commonly used to find optimal solutions and count the total number of solutions for various problems.
01:32Dynamic programming was invented by Richard Bellman in the 1950s.
06:12The key idea behind dynamic programming is to identify and solve subproblems that can help solve the main problem.
11:27Dynamic programming can be implemented using memorization (top-down) or a bottom-up approach.
