Dynamic programming is a way of solving complex problems by dividing them into smaller sub problems and finding solution for those sub problems. We accumulate the solutions of sub problems to find the global solution. The good part of dynamic programming is that we reduce the recalculation of sub problems by storing their results. Dynamic programming is a very well-known method for optimization. The use of dynamic algorithm can be found everywhere in the programming world. Dynamic programming can solve problems such as coin changing, finding the longest common subsequence, finding the longest increasing sequences, sequencing DNA strings, and so on. The core difference between the greedy algorithm and dynamic programming is that dynamic programming will always prefer a globally optimized solution.
We can solve a problem with dynamic programming...
