- AApplication code
- BType of programming language
- CStep by step procedure for calculations
- DNone of above
Greedy Algorithms - Quiz
- A Solving a problem by breaking it down into smaller subproblems
- BGenerating all possible solutions and selecting the best one
- C Repeating the same steps over and over until a solution is found
- DMaking the locally optimal choice at each step to obtain a globally optimal solution
- A Finding the shortest path between two nodes in a graph
- BComputing the greatest common divisor of two numbers
- CSorting a list of integers
- D The coin change problem
- A O(n)
- B O(n log n)
- CO(n^2)
- DO(log n)
- A It always guarantees the optimal solution
- BIt can be slow for large input sizes
- CIt requires a lot of memory
- DIt may not always find the globally optimal solution
- AO(n)
- BO(n log n)
- CO(n^2)
- DO(log n)
- A It always guarantees the optimal solution
- BIt can be slow for large input sizes
- C It requires a lot of memory
- DIt may not always find the globally optimal solution
- AFinding the shortest path between two nodes in a graph
- B Computing the greatest common divisor of two numbers
- CSorting a list of integers
- DThe traveling salesman problem
- AThe knapsack problem
- BThe minimum spanning tree problem
- CThe job scheduling problem
- DThe longest common subsequence problem
- ASelecting a subset of intervals that do not overlap
- BSorting the intervals by start time and selecting the earliest start time
- CSorting the intervals by end time and selecting the earliest end time
- DGenerating all possible subsets of intervals and selecting the one with the most intervals
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Greedy Algorithms - Quiz
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- A. Application code
- B. Type of programming language
- C. Step by step procedure for calculations
- D. None of above
Remarks:
Explanation: Step by step procedure for calculations
- A. Solving a problem by breaking it down into smaller subproblems
- B. Generating all possible solutions and selecting the best one
- C. Repeating the same steps over and over until a solution is found
- D. Making the locally optimal choice at each step to obtain a globally optimal solution
Remarks:
Explanation:
Answer: Making the locally optimal choice at each step to obtain a globally optimal solution
Explanation: A greedy algorithm makes the locally optimal choice at each step in the hope of finding a globally optimal solution. The algorithm chooses the best option available at each step without considering the future consequences.
- A. Finding the shortest path between two nodes in a graph
- B. Computing the greatest common divisor of two numbers
- C. Sorting a list of integers
- D. The coin change problem
Remarks:
Explanation:
Answer: The coin change problem
Explanation: The coin change problem can be solved using a greedy algorithm, where the algorithm selects the largest denomination of coin possible at each step until the total amount is reached.
- A. O(n)
- B. O(n log n)
- C. O(n^2)
- D. O(log n)
Remarks:
Explanation:
Answer: O(n)
Explanation: The greedy algorithm for the coin change problem has a time complexity of O(n), where n is the number of coins used to make the change.
- A. It always guarantees the optimal solution
- B. It can be slow for large input sizes
- C. It requires a lot of memory
- D. It may not always find the globally optimal solution
Remarks:
Explanation:
Answer: It may not always find the globally optimal solution
Explanation: A greedy algorithm may not always find the globally optimal solution since it makes locally optimal choices at each step without considering the future consequences. This can lead to suboptimal solutions for some problems.
- A. O(n)
- B. O(n log n)
- C. O(n^2)
- D. O(log n)
Remarks:
Explanation:
Answer: O(n log n)
Explanation: The Huffman coding algorithm has a time complexity of O(n log n), where n is the number of characters to be encoded.
- A. It always guarantees the optimal solution
- B. It can be slow for large input sizes
- C. It requires a lot of memory
- D. It may not always find the globally optimal solution
Remarks:
Explanation:
Answer: It may not always find the globally optimal solution
Explanation: The Huffman coding algorithm may not always find the globally optimal solution since it makes locally optimal choices at each step.- A. Finding the shortest path between two nodes in a graph
- B. Computing the greatest common divisor of two numbers
- C. Sorting a list of integers
- D. The traveling salesman problem
Remarks:
Explanation:
Answer : The traveling salesman problem
Explanation: The traveling salesman problem is a well-known problem that cannot be solved using a greedy algorithm since it requires examining all possible permutations of the cities to find the shortest possible route.
- A. The knapsack problem
- B. The minimum spanning tree problem
- C. The job scheduling problem
- D. The longest common subsequence problem
Remarks:
Explanation:
Answer: The minimum spanning tree problem
Explanation: The minimum spanning tree problem can be solved using a greedy algorithm called Kruskal's algorithm, which has a proof of optimality.
- A. Selecting a subset of intervals that do not overlap
- B. Sorting the intervals by start time and selecting the earliest start time
- C. Sorting the intervals by end time and selecting the earliest end time
- D. Generating all possible subsets of intervals and selecting the one with the most intervals
Remarks:
Explanation:
Answer: Selecting a subset of intervals that do not overlap
Explanation: The interval scheduling problem involves selecting a subset of intervals that do not overlap, with the goal of selecting the maximum number of intervals possible. A greedy algorithm can be used to solve this problem by selecting the interval with the earliest end time, and then selecting the next interval with the earliest end time that does not overlap with the previously selected intervals.