In dynamic programming, what is the purpose of the "bottom-up" approach?
- To avoid recursion and use iterative loops for optimization.
- To skip solving subproblems and directly compute the final answer.
- To solve smaller subproblems first before tackling larger ones.
- To start solving the problem from the largest subproblem size.
The bottom-up approach in dynamic programming involves solving smaller subproblems first and then combining their solutions to solve larger subproblems. This approach is typically more efficient than the top-down approach because it avoids redundant computations and optimizes the use of memory. By starting from the smallest subproblems and gradually building up to the final solution, bottom-up dynamic programming ensures that each subproblem is solved only once and its result is stored for future use, reducing computational overhead.
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