In a social network application, you need to find the shortest path between two users based on mutual friends. Would BFS be suitable for this task, or would another algorithm be more appropriate?
- A* Algorithm
- Breadth-First Search (BFS)
- Depth-First Search (DFS)
- Dijkstra's Algorithm
BFS would be suitable for finding the shortest path based on mutual friends in a social network. BFS explores neighbors first, making it effective for finding mutual connections. Other algorithms like DFS may not guarantee the shortest path and Dijkstra's Algorithm is more suitable for weighted graphs, which may not be relevant in a social network context.
In selection sort, what is the main operation performed in each iteration?
- Doubling the size of the sorted portion
- Finding the minimum element in the unsorted portion and swapping it with the first element of the unsorted part
- Multiplying elements in the unsorted portion
- Randomly rearranging elements in the unsorted portion
The main operation in each iteration of selection sort is finding the minimum element in the unsorted portion and swapping it with the first element of the unsorted part. This gradually builds the sorted portion.
What is the main disadvantage of the basic implementation of Quick Sort?
- Limited applicability
- Not in-place
- Poor performance on small datasets
- Unstable sorting
The main disadvantage of the basic implementation of Quick Sort is its poor performance on small datasets. While efficient for large datasets, it may not be the best choice for smaller ones due to overhead in the recursive calls and partitioning.
Quick Sort can handle duplicate elements efficiently due to its _______ step.
- Merging
- Partitioning
- Searching
- Sorting
Quick Sort handles duplicate elements efficiently due to its partitioning step, where elements are rearranged such that duplicates end up together, making the subsequent steps more efficient.
What is the time complexity of the dynamic programming approach for solving the longest common substring problem?
- O(n log n)
- O(n)
- O(n^2)
- O(n^3)
The time complexity of the dynamic programming approach for the longest common substring problem is O(n^2), where 'n' is the length of the input strings. This is achieved by using a 2D table to store intermediate results and avoiding redundant computations.
How does the A* search algorithm differ from other search algorithms like Depth-First Search and Breadth-First Search?
- A* combines both the depth-first and breadth-first approaches
- A* considers only the breadth-first approach
- A* considers only the depth-first approach
- A* has no similarities with Depth-First and Breadth-First Search
A* search algorithm differs from others by combining elements of both depth-first and breadth-first approaches. It uses a heuristic to guide the search, unlike the purely blind search of Depth-First and Breadth-First Search.
To find the shortest path in a weighted graph using BFS, one can modify the algorithm to use _______ for determining the next node to explore.
- Binary Search Tree
- Linked List
- Priority Queue
- Stack
To find the shortest path in a weighted graph using BFS, one can modify the algorithm to use a priority queue for determining the next node to explore. This allows selecting the node with the minimum distance efficiently.
Discuss the trade-offs between using a fixed-size hash table versus a dynamically resizing hash table.
- Both fixed-size and dynamically resizing hash tables have the same space complexity. The only difference is in their time complexity for insertion and deletion operations.
- Fixed-size hash tables are always preferable due to their simplicity and lack of memory management concerns.
- Fixed-size hash tables dynamically adjust their size based on the number of elements, while dynamically resizing hash tables maintain a constant size.
- Fixed-size hash tables offer constant space complexity but may lead to collisions. Dynamically resizing hash tables adapt to the number of elements but incur additional memory management overhead.
The trade-offs between fixed-size and dynamically resizing hash tables involve space complexity and adaptability. Fixed-size tables offer constant space complexity but may lead to collisions when the number of elements grows. Dynamically resizing tables adjust their size to accommodate the number of elements but introduce memory management overhead and potential performance hits during resizing operations.
The dynamic programming approach to solving Edit Distance involves constructing a _______ to store intermediate results.
- Hash table
- Matrix
- Queue
- Stack
The dynamic programming approach for Edit Distance involves constructing a matrix to store intermediate results. Each cell in the matrix represents the minimum number of operations required to transform substrings of the two input strings.
How do you find the middle element of a singly linked list in one pass?
- Iterate through the list, counting the number of elements, and then traverse the list again to the middle element.
- There is no efficient way to find the middle element in one pass for a singly linked list.
- Use recursion to find the middle element efficiently.
- Use two pointers, one moving at twice the speed of the other. When the faster pointer reaches the end, the slower pointer will be at the middle element.
By using two pointers, one moving at twice the speed of the other, you can efficiently find the middle element in one pass. The faster pointer reaches the end while the slower pointer points to the middle element.