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Questions and Answers
Which of the following is an example of a situation where dynamic programming would be most applicable?
Which of the following is an example of a situation where dynamic programming would be most applicable?
- Sorting a list of one million randomly ordered integers using the quicksort algorithm.
- Searching for a specific entry in a balanced binary search tree.
- Finding the shortest path between two cities on a road map, given traffic conditions change every hour. (correct)
- Calculating the factorial of a very large number without storing intermediate results.
What is the primary difference between a greedy algorithm and dynamic programming when solving optimization problems?
What is the primary difference between a greedy algorithm and dynamic programming when solving optimization problems?
- Dynamic programming is typically faster for solving very large problems compared to greedy algorithms.
- Greedy algorithms use more memory than dynamic programming.
- Greedy algorithms make locally optimal choices without considering the overall problem, while dynamic programming considers all possible subproblems. (correct)
- Greedy algorithms guarantee an optimal solution, whereas dynamic programming provides an approximate one.
Consider a scenario where you need to compute the nth Fibonacci number using dynamic programming. Which approach is more space-efficient, and why?
Consider a scenario where you need to compute the nth Fibonacci number using dynamic programming. Which approach is more space-efficient, and why?
- Top-down memoization, because it stores all computed Fibonacci numbers in a table.
- Bottom-up tabulation, because it avoids recursion overhead.
- Bottom-up tabulation, because it only needs to store the last two computed Fibonacci numbers. (correct)
- Top-down memoization, because it only computes the Fibonacci numbers it needs.
In the context of dynamic programming, what does the term 'optimal substructure' refer to?
In the context of dynamic programming, what does the term 'optimal substructure' refer to?
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You are tasked with implementing an algorithm to determine the minimum number of coins needed to make change for a given amount, using a set of coin denominations. Which algorithmic approach is most suitable for efficiently solving this problem?
You are tasked with implementing an algorithm to determine the minimum number of coins needed to make change for a given amount, using a set of coin denominations. Which algorithmic approach is most suitable for efficiently solving this problem?
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