How to approach Dynamic Programming Problems: A comprehensive tutorial for Beginners

Dynamic programming (DP) is one of the trickiest algorithm techniques. To simplify this DSA class, I created this tutorial to discuss and provide solutions to strategically selected dynamic programming problems that transcend from the beginner to intermediate level of difficulty.

I will explain the logic behind the problems and share the code, allowing you to intuitively apply those approaches in similar DP problems. The problems covered in this article are: Maximum Subset Sum with No Adjacent, Number of Ways to Make Change, Minimum Number of Coins for Change, Levenshtein Distance, and Number of ways to Traverse a Graph.

Overview:

To effectively solve a dynamic programming question, we need to think about the following concepts. Base case, recursive function, DP array. I would explore this in greater detail within the context of the problems, but here are the definitions so that the concepts are less foreign.

• The base case: The base case defines the simplest possible subproblem that cannot be broken down further. It’s the foundation upon which…