While topological sorting primarily applies to directed acyclic graphs (DAGs), certain algorithms can handle graphs with cyclic edges by modifying the approach. Handling cycles requires additional considerations and modifications to traditional topological sorting algorithms.

Parenthesization in the context of Matrix Chain Multiplication refers to the placement of parentheses to determine the order in which matrices are multiplied. Dynamic programming helps find the optimal parenthesization to minimize the overall computational cost.

The choice of compression algorithm can impact the decompression process as different algorithms may require different techniques to reconstruct the original string. The efficiency and correctness of decompression can vary based on the chosen algorithm.

Dijkstra's algorithm has a time complexity of O((V + E) * log V) using a binary heap. Various optimizations can be applied, such as using a Fibonacci heap to achieve a time complexity of O(V log V + E log V). These optimizations aim to reduce the overall complexity, making Dijkstra's algorithm more efficient for large graphs.

Manacher's Algorithm exploits the symmetry of palindromes to achieve linear time complexity. It cleverly uses information from previously processed characters to avoid redundant computations, making it an efficient algorithm for finding palindromic substrings.

Merge sort can efficiently handle the sorting of linked lists. Unlike array-based sorting algorithms, merge sort's divide-and-conquer approach is well-suited for linked lists as it involves splitting and merging without the need for random access to elements. This makes it a preferred choice for sorting linked structures.

The time complexity of merge sort in the worst-case scenario is O(n log n), making it an efficient algorithm for sorting large datasets. This complexity arises from its divide-and-conquer approach.

BFS guarantees finding the shortest path in an unweighted graph due to its Breadth-First approach. This means it explores all nodes at the current depth before moving on to nodes at the next depth level, ensuring that the shortest path is found first.

Finding the Longest Common Subsequence (LCS) has significant applications in tasks such as genome sequencing, where identifying common elements in sequences is vital, and version control systems, where it helps track changes in code or documents.

In each iteration of the inner loop in selection sort, where 'i' is the current iteration, n - i comparisons are performed. This is because the inner loop looks for the minimum element in the unsorted portion and places it at the beginning, reducing the number of comparisons in subsequent iterations.