Top Down Dynamic Programming. We divide the large problem into multiple subproblems. The first dynamic programming approach well use is the top down approach.
That is why we store the result of sub problems in a table so that we dont have to compute the result of a same sub problem again and again. This is the direct fall out of the recursive formulation of any problem. The top down approach solves dynamic programming problems by attempting to solve the largest subproblem final optimal solution first.
Memoization method top down dynamic programming once again lets describe it in terms of state transition.
The bottom up approach to dynamic programming consists in first looking at the smaller subproblems and then solve the larger subproblems using the solution to the smaller problems. This may sound similar to the heavily used algorithmic technique divide and conquer however there is a key distinction. Approaches of dynamic programming. Memoization method top down dynamic programming once again lets describe it in terms of state transition.