Insertion Sort works by incrementally building the sorted subarray. It starts with a single element and gradually adds more elements to the sorted subarray by shifting elements to their correct positions. This process is repeated until the entire array is sorted.

Radix sort is not suitable for sorting strings with variable lengths. It operates based on the position of digits, making it more suitable for fixed-length integers. For variable-length strings like names, merge sort would be a better choice, as it doesn't rely on specific positions.

In merge sort, the process of merging two sorted subarrays into a single sorted array is known as merging. This step is crucial for achieving the overall sorted order of the elements in the array.

Matrix exponentiation provides a method to compute Fibonacci numbers with O(log n) time complexity. This efficient algorithm is especially advantageous for large values of n compared to the traditional recursive approach with higher time complexity.

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.

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.

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.

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.

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.

The typical constraints in the Knapsack Problem include the weight and value of the items. These constraints ensure that the selected items do not exceed the capacity of the knapsack while maximizing the total value.

Breadth-First Search (BFS) is commonly used in network routing for finding the shortest path between two nodes. It explores nodes level by level, making it efficient for finding the shortest path in networks.

In the context of the Longest Increasing Subsequence problem, "increasing" refers to the sequence where each element is Larger than the previous one. The goal is to find the longest subsequence where each element is strictly increasing.