What is the time complexity of Breadth-First Search (BFS) for traversing a graph with V vertices and E edges?
- O(V * E)
- O(V + E)
- O(V^2)
- O(log V)
The time complexity of BFS for traversing a graph with V vertices and E edges is O(V + E), as each vertex and edge is visited once. This linear complexity is advantageous for sparse graphs.
How does the patience sorting algorithm relate to the Longest Increasing Subsequence problem?
- It is a sorting algorithm specifically designed for the Longest Increasing Subsequence problem.
- It is an alternative name for the Longest Increasing Subsequence problem.
- It is unrelated to the Longest Increasing Subsequence problem.
- Patience sorting is a solution strategy for the Longest Increasing Subsequence problem.
The patience sorting algorithm is related to the Longest Increasing Subsequence (LIS) problem as it provides a strategy to find the length of the LIS. The concept involves simulating a card game where each card represents an element in the sequence, and the goal is to build piles with specific rules to determine the LIS.
How does the Edit Distance algorithm handle cases where the two strings have different lengths?
- It automatically pads the shorter string with extra characters to make them equal in length.
- It handles different lengths by introducing additional operations such as insertion or deletion.
- It raises an error since the strings must have the same length.
- It truncates the longer string to match the length of the shorter string.
The Edit Distance algorithm handles cases with different lengths by introducing additional operations (insertion or deletion) to account for the difference, ensuring a comprehensive comparison between the two strings.
How can you handle deletions efficiently in a hash table while maintaining performance?
- Deleting the element and shifting all subsequent elements one position to the left.
- Marking the deleted elements as "deleted" and skipping them during searches.
- Relocating all elements in the table to fill the gap left by the deleted element.
- Simply removing the element from the hash table and leaving the space empty.
Efficient deletion in a hash table involves marking the deleted elements as "deleted" and skipping them during searches. This approach prevents disruptions in the hash table's structure and maintains performance by avoiding unnecessary shifting or relocating of elements.
How does the Rabin-Karp algorithm handle potential spurious matches?
- It adjusts the length of the search window dynamically to avoid spurious matches.
- It ignores potential spurious matches and relies on a post-processing step to filter them out.
- It rehashes the entire text for each potential match to verify its accuracy.
- It uses a rolling hash function to efficiently update the hash value of the current window.
The Rabin-Karp algorithm handles potential spurious matches by using a rolling hash function. This allows it to efficiently update the hash value of the current window in constant time, reducing the likelihood of false positives.
How does Prim's algorithm select the next vertex to add to the minimum spanning tree?
- Chooses the vertex with the highest degree.
- Chooses the vertex with the maximum key value among the vertices not yet included in the minimum spanning tree.
- Chooses the vertex with the minimum key value among the vertices not yet included in the minimum spanning tree.
- Randomly selects a vertex from the graph.
Prim's algorithm selects the next vertex to add to the minimum spanning tree based on the minimum key value among the vertices not yet included in the tree. The key value represents the weight of the smallest edge connecting the vertex to the current minimum spanning tree.
Imagine you are tasked with optimizing the performance of a web application that heavily relies on regular expressions for URL routing and validation. What strategies would you employ to improve the speed and efficiency of regular expression matching in this context?
- Caching frequently used regular expressions
- Increasing the complexity of regular expressions for better specificity
- Reducing the number of regular expressions used
- Utilizing backtracking for flexibility
To improve the speed and efficiency of regular expression matching in a web application, caching frequently used regular expressions is a viable strategy. This helps avoid redundant compilation and evaluation of the same patterns, contributing to overall performance optimization.
The worst-case time complexity of bubble sort is _______.
- O(log n)
- O(n log n)
- O(n)
- O(n^2)
The worst-case time complexity of bubble sort is O(n^2), where 'n' is the number of elements in the array. This is due to the nested loops that iterate over the elements, making it inefficient.
The Ford-Fulkerson algorithm aims to find the _______ flow in a network graph.
- Balanced
- Maximum
- Minimum
- Optimal
The Ford-Fulkerson algorithm aims to find the maximum flow in a network graph, which represents the maximum amount of flow that can be sent from a designated source to a designated sink in a network.
The Ford-Fulkerson algorithm relies on the concept of _______ to incrementally improve the flow.
- Augmentation
- Contraction
- Expansion
- Subgraph
The Ford-Fulkerson algorithm relies on the concept of augmentation to incrementally improve the flow. Augmentation involves finding an augmenting path in the residual graph and updating the flow values along that path.