The major required aspects in a problem in order to apply dynamic programming technique exists optimal Substructure and Overlapping subproblems.
A problem is said to have an optimal substructure in computer science if an optimal solution can be built from optimal solutions of its subproblems. This characteristic is used to assess the applicability of greedy algorithms to a given task.
I am aware of both methods' target approaches: Overlapping Subproblems targets all solutions for the input range, say from 1 to n, while Optimal Substructure aims the optimal solution based on an input n.
If an issue can be split down into subproblems that can be solved repeatedly or if a recursive solution for the problem solves the same subproblem repeatedly rather than creating new subproblems every time, it is said to have overlapping subproblems in computer science.
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