LCS can be applied to non-string data types such as _______ to find common elements in sequences.
- Arrays, Linked lists
- Numbers, Matrices
- Stacks, Queues
- Trees, Graphs
Longest Common Subsequence (LCS) is a versatile algorithm that can be applied to non-string data types such as trees and graphs. It is used to identify common elements in sequences, providing a valuable tool in various domains beyond traditional string processing.
How does merge sort divide and conquer a given list/array?
- It multiplies each element by a random factor
- It randomly splits the list into parts
- It recursively divides the list into halves, sorts each half, and then merges them back together.
- It selects the smallest element and moves it to the beginning
Merge sort divides a given list or array by recursively breaking it into halves until individual elements. Then, it sorts each segment and merges them back together to construct a sorted array.
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.
Which data structure is typically used to implement binary search efficiently?
- Linked List
- Queue
- Sorted Array
- Stack
Binary search is typically implemented on a sorted array. This is because the algorithm relies on the ability to efficiently discard half of the elements based on a comparison with the target value.
What are some common use cases for regular expression matching?
- Calculating mathematical expressions, generating random numbers, formatting dates.
- Copying files between directories, creating network connections, compiling source code.
- Playing multimedia files, encrypting data, compressing files.
- Validating email addresses, searching for specific words in a document, extracting data from text, and pattern-based substitutions.
Common use cases for regular expression matching include validating email addresses, searching for specific words in a document, extracting data from text, and performing pattern-based substitutions. Regular expressions provide a flexible and efficient way to work with textual data.