- A Solving a problem by breaking it down into smaller subproblems
- BGenerating all possible solutions and selecting the best one
- CRepeating the same steps over and over until a solution is found
- DSearching for a solution by gradually eliminating possibilities
Divide and Conquer - Quiz
- ASorting a list of integers
- B Finding the shortest path between two nodes in a graph
- C Computing the factorial of a number
- DComputing the greatest common divisor of two numbers
- AO(n)
- BO(n log n)
- CO(n^2)
- DO(log n)
- A It is often slower than other approaches
- B It requires a lot of memory
- CIt can be difficult to implement
- DIt may involve redundant computations
- ABreaking a problem down into smaller subproblems
- BGenerating all possible solutions and selecting the best one
- CRepeating the same steps over and over until a solution is found
- DSearching for a solution by gradually eliminating possibilities
- AO(n)
- BO(n log n)
- CO(log n)
- DO(n^2)
- AIt only works on sorted lists
- BIt is slower than linear search for small lists
- CIt requires a lot of memory
- DIt can be difficult to implement
- AFinding the shortest path between two nodes in a graph
- BComputing the greatest common divisor of two numbers
- CFinding the maximum subarray sum
- D Computing the factorial of a number
- AComputing the nth Fibonacci number
- BFinding the median of two sorted arrays
- CSorting a linked list
- DFinding the shortest path between two nodes in a graph
- AIt always guarantees the optimal solution
- B It is easy to implement
- CIt is efficient for all types of problems
- DIt can be parallelized to run on multiple processors
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Divide and Conquer - Quiz
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- 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. Searching for a solution by gradually eliminating possibilities
Remarks:
Explanation:
Answer: Solving a problem by breaking it down into smaller subproblems
Explanation: The divide and conquer approach involves breaking a problem down into smaller subproblems and solving each subproblem independently. The solutions to the subproblems are then combined to solve the original problem.
- A. Sorting a list of integers
- B. Finding the shortest path between two nodes in a graph
- C. Computing the factorial of a number
- D. Computing the greatest common divisor of two numbers
Remarks:
Explanation:
Answer: Sorting a list of integers
Explanation: Sorting a list of integers can be done using the divide and conquer approach, such as merge sort or quicksort. The list is divided into smaller sublists, sorted independently, and then merged together.
- 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 merge sort algorithm has a time complexity of O(n log n), which is considered efficient. It involves dividing the list into smaller sublists, sorting them independently, and then merging them together.
- A. It is often slower than other approaches
- B. It requires a lot of memory
- C. It can be difficult to implement
- D. It may involve redundant computations
Remarks:
Explanation:
Answer: It requires a lot of memory
Explanation: The divide and conquer approach often involves breaking a problem down into smaller subproblems, which may require creating new data structures in memory. This can lead to high memory usage, which is a disadvantage of the approach.
- A. Breaking a problem 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. Searching for a solution by gradually eliminating possibilities
Remarks:
Explanation:
Answer: Searching for a solution by gradually eliminating possibilities
Explanation: The binary search algorithm involves searching for a target value in a sorted list by repeatedly dividing the list in half and eliminating one half of the remaining possibilities at each step.
- A. O(n)
- B. O(n log n)
- C. O(log n)
- D. O(n^2)
Remarks:
Explanation:
Answer: O(log n)
Explanation: The binary search algorithm has a time complexity of O(log n), which is considered efficient. It involves repeatedly dividing the search space in half until the target value is found or determined to not exist.
- A. It only works on sorted lists
- B. It is slower than linear search for small lists
- C. It requires a lot of memory
- D. It can be difficult to implement
Remarks:
Explanation:
Answer: It only works on sorted lists
Explanation: The binary search algorithm requires that the list be sorted, which can be a disadvantage if the list is not already sorted or if sorting it is expensive.
- A. Finding the shortest path between two nodes in a graph
- B. Computing the greatest common divisor of two numbers
- C. Finding the maximum subarray sum
- D. Computing the factorial of a number
Remarks:
Explanation:
Answer: Computing the greatest common divisor of two numbers
Explanation: The binary search approach can be used to efficiently compute the greatest common divisor of two numbers by repeatedly dividing the larger number by the smaller number until the remainder is 0.
- A. Computing the nth Fibonacci number
- B. Finding the median of two sorted arrays
- C. Sorting a linked list
- D. Finding the shortest path between two nodes in a graph
Remarks:
Explanation:
Answer: Finding the shortest path between two nodes in a graph
Explanation: While some graph problems can be solved using the divide and conquer approach, finding the shortest path between two nodes typically requires a different algorithm, such as Dijkstra's algorithm or Bellman-Ford algorithm.
- A. It always guarantees the optimal solution
- B. It is easy to implement
- C. It is efficient for all types of problems
- D. It can be parallelized to run on multiple processors
Remarks:
Explanation:
Answer: It can be parallelized to run on multiple processors
Explanation: The divide and conquer approach can be parallelized to run on multiple processors, which can result in faster execution times. This is an advantage of the approach for some problems, especially those with large input sizes.