Can you explain the concept of lossless and lossy compression in the context of string compression algorithms?
- Lossless compression discards some data during compression but can fully recover the original data during decompression.
- Lossless compression retains all original data during compression and decompression.
- Lossy compression intentionally discards some data during compression, and the lost data cannot be fully recovered during decompression.
- Lossy compression retains all original data during compression but sacrifices some data during decompression.
In the context of string compression algorithms, lossless compression retains all original data during compression and decompression. On the other hand, lossy compression intentionally discards some data during compression, and the lost data cannot be fully recovered during decompression. The choice between lossless and lossy compression depends on the application's requirements and the acceptable level of data loss.
What does topological sorting primarily aim to do in a directed graph?
- Arranges the vertices in a linear order such that for every directed edge (u, v), vertex u comes before vertex v in the order.
- Finds the shortest path between two vertices in the graph.
- Identifies cycles in the graph.
- Rearranges the vertices randomly.
Topological sorting in a directed graph aims to arrange the vertices in a linear order such that for every directed edge (u, v), vertex u comes before vertex v in the order. This order is often used to represent dependencies between tasks or events.
Discuss the space complexity of merge sort and how it compares to other sorting algorithms.
- O(log n)
- O(n log n)
- O(n)
- O(n^2)
Merge sort has a space complexity of O(n) due to its need for additional memory. This is more efficient than algorithms with higher space complexity, like quicksort with O(n^2) in the worst case, making merge sort advantageous in terms of space usage.
Selection sort is not suitable for _______ datasets as it performs a fixed number of comparisons and swaps.
- Large
- Randomized
- Small
- Sorted
Selection sort is not suitable for large datasets as it performs a fixed number of comparisons and swaps. Regardless of the input, it always performs the same number of operations, making it inefficient for large datasets.
You're developing software for a ride-sharing service. How might you use a queue to handle incoming ride requests and allocate drivers to passengers?
- Allocate drivers based on a first-come, first-served basis from the queue.
- Assign drivers based on random selection for variety.
- Implement a queue where the longest waiting driver is assigned to the next ride.
- Use a priority queue to allocate drivers based on passenger ratings.
In a ride-sharing service, using a queue for driver allocation involves assigning drivers on a first-come, first-served basis from the queue. This ensures fairness and efficiency in handling incoming ride requests.
Can merge sort be easily implemented in parallel processing environments? Explain.
- It depends on the dataset characteristics
- No, it is a strictly sequential algorithm
- Only in specific cases
- Yes, it is well-suited for parallel processing
Merge sort is inherently suitable for parallel processing as its divide-and-conquer nature allows for concurrent processing of subproblems. Each recursive call can be executed independently, making it an efficient choice for parallel architectures.
The Edit Distance algorithm computes the minimum number of _______ operations required to transform one string into another.
- Addition
- Deletion
- Substitution
- All of the above
The Edit Distance algorithm considers three possible operations: addition, deletion, and substitution. It computes the minimum number of these operations required to transform one string into another, making option 4, "All of the above," the correct choice.
Arrays provide _______ access to elements, but inserting or deleting elements can be _______.
- Constant, complex
- Direct, inefficient
- Random, time-consuming
- Sequential, fast
Arrays provide sequential access to elements, meaning that elements are stored in contiguous memory locations. However, inserting or deleting elements in the middle of an array can be time-consuming and inefficient, as it may require shifting all subsequent elements.
In which scenario would you choose Dijkstra's algorithm over Bellman-Ford or Floyd-Warshall algorithms?
- In scenarios where the graph has cycles.
- When dealing with a graph with negative edge weights.
- When the graph has both positive and negative edge weights.
- When working with a graph with non-negative edge weights.
Dijkstra's algorithm is preferred over Bellman-Ford or Floyd-Warshall algorithms when working with a graph that has non-negative edge weights. Unlike Bellman-Ford, Dijkstra's algorithm does not handle negative weights and is more efficient in such scenarios.
BFS, nodes are visited level by level, starting from the _______ node.
- Intermediate
- Leaf
- Random
- Root
In BFS (Breadth-First Search), nodes are visited level by level, starting from the root node. The algorithm explores all nodes at the current level before moving to the next level.