Dynamic programming stores solutions to subproblems for efficiency from "summary" of Data Structures and Algorithms in Python by Michael T. Goodrich,Roberto Tamassia,Michael H. Goldwasser
Dynamic programming involves breaking down a complex problem into simpler subproblems and solving each subproblem only once. The key idea behind dynamic programming is to store the solutions to subproblems so that they can be reused when needed, avoiding redundant computations. By storing the solutions to subproblems, dynamic programming can greatly improve the efficiency of algorithms. When applying dynamic programming to a problem, we first identify the subproblems that need to be solved. ...
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