Dynamic programming involves breaking down problems into their subproblems. By solving for the optimal subproblems and saving those results into memory to access them whenever a repeated problem needs to be solved, the algorithmic complexity decreases significantly. Implementing dynamic programming algorithms requires higher-level thinking about the problem’s patterns. To explain dynamic programming, let’s re-examine the Fibonacci sequence that was discussed in Chapter 8. Then the chapter will cover the rules of dynamic programming and walk you through some examples to make the concepts more concrete.