Should I learn Recursion before dynamic programming?

DP is a type of algorithm that breaks down problems into sub-problems and stores and reuses the results from the previous calculations. We shall introduce what a recursion is before DP. The recursive function calls itself and re-calculates the results to sub-problems repetitively, which is inefficient.

What math do I need for competitive programming?

Competitive Programming (CP) doesn’t typically require to know high-level calculus or some rocket science. But there are some concepts and tricks which are sufficient most of the times. You can definitely start competitive coding without any mathematical background.

How do I know my DP?

Specifically, I will go through the following steps:

1. How to recognize a DP problem.
2. Identify problem variables.
3. Clearly express the recurrence relation.
4. Identify the base cases.
5. Decide if you want to implement it iteratively or recursively.
6. Add memoization.
7. Determine time complexity.

Is DP same as recursion?

Remember, dynamic programming should not be confused with recursion. Recursion takes time but no space while dynamic programming uses space to store solutions to subproblems for future reference thus saving time.

How do you master recursion and dynamic programming?

The best way to practice dynamic programming is:

1. First, define a brute force recursive solution.
2. Characterise the structure of the recursive solution.
3. Identify the base cases.
4. Store the computed values of overlapping subproblems.
5. Convert Recursive code to Memoised code.
6. Convert Memoised code to Tabular form.

How do I start dynamic programming?

7 Steps to solve a Dynamic Programming problem

1. How to recognize a DP problem.
2. Identify problem variables.
3. Clearly express the recurrence relation.
4. Identify the base cases.
5. Decide if you want to implement it iteratively or recursively.
6. Add memoization.
7. Determine time complexity.

What is need of dynamic programming?

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.