What is dynamic programming and what are the different approaches used to optimize problems.

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Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems and storing the results to avoid redundant calculations.

Dynamic programming involves breaking down a problem into smaller subproblems and solving each subproblem only once.
There are two main approaches to optimize problems using dynamic programming: top-down (memoization) and bottom-up (tabulation).
Memoization involves storing the results of subproblems in a cache to avoid redundant calculations.
Tabulation involves solving subproblems in a bottom-up manner and storing the results in a table.
Examples of dynamic programming problems include the Fibonacci sequence, shortest path problems, and the knapsack problem.

What is dynamic programming and what are the different approaches used to optimize problems.

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