An efficient way to handle deletions in a hash table is to use a special marker value to mark deleted entries. This allows for proper rehashing and ensures that the deleted entries are correctly accounted for during subsequent operations.

The selection sort algorithm is a simple sorting algorithm that divides the input list into two parts: a sorted and an unsorted portion. It repeatedly selects the smallest (or largest) element from the unsorted part and swaps it with the first element of the unsorted part.

One application of DFS is in pathfinding and graph traversal problems. It is commonly used to find paths between nodes in a graph or to explore all nodes in a graph.

Radix sort is suitable for sorting data with a fixed size because it processes each digit separately, allowing it to handle numbers with varying lengths in a more efficient manner.

In a priority queue, elements are arranged for retrieval based on a specific priority assigned to each element. The element with the highest priority is retrieved first. This ensures that higher-priority elements take precedence over lower-priority ones.

Bellman-Ford algorithm can handle graphs with variable edge weights and detect negative weight cycles. It is capable of handling graphs with both positive and negative edge weights, making it suitable for a wider range of scenarios compared to some other algorithms.

Memoization is an optimization technique where intermediate results are stored, preventing redundant calculations and significantly improving the efficiency of the Edit Distance algorithm.

A* search combines the advantages of the Greedy algorithm, which prioritizes nodes based on a heuristic, and Dijkstra's algorithm, which ensures the shortest path. This combination allows A* to efficiently find the optimal path by considering both the heuristic information and the actual cost of reaching the node.

To optimize the Ford-Fulkerson algorithm, one can explore preprocessing techniques to reduce the number of iterations. Preprocessing involves modifying the graph or the initial flow to simplify subsequent iterations, potentially accelerating the convergence of the algorithm.

BFS guarantees finding the shortest path in an unweighted graph because it explores nodes in increasing order. As it systematically traverses nodes level by level, the first time a node is encountered, it is reached through the shortest path.