Worst Case Time Complexity of Algorithms

Data Structures:

• Array: Access O(1), Search O(n), Insertion O(n), Deletion O(n)

• Linked List: Access O(n), Search O(n), Insertion O(1), Deletion O(1)

• Stack: Access O(n), Search O(n), Insertion O(1), Deletion O(1)

• Queue: Access O(n), Search O(n), Insertion O(1), Deletion O(1)

• Binary Search Tree: Access O(log n), Search O(log n), Insertion O(log n), Deletion O(log n)

• Heap: Access O(1), Search O(n), Insertion O(log n), Deletion O(log n)

• Hash Table: Access O(1), Search O(1), Insertion O(1), Deletion O(1)

Algorithms:

• Linear Search: O(n)

• Binary Search: O(log n)

• Bubble Sort: O(n^2)

• Selection Sort: O(n^2)

• Insertion Sort: O(n^2)

• Quick Sort: O(n log n) average case, O(n^2) worst case

• Merge Sort: O(n log n)

• Heap Sort: O(n log n)

• Counting Sort: O(n + k), where k is the range of the input

• Radix Sort: O(d(n+k)), where d is the number of digits in the maximum number and k is the range of the input

• Breadth-First Search: O(V + E), where V is the number of vertices and E is the number of edges in the graph

• Depth-First Search: O(V + E), where V is the number of vertices and E is the number of edges in the graph

• Dijkstra's Algorithm: O((V+E) log V), where V is the number of vertices and E is the number of edges in the graph

• Bellman-Ford Algorithm: O(VE), where V is the number of vertices and E is the number of edges in the graph

• Floyd-Warshall Algorithm: O(V^3), where V is the number of vertices in the graph

Note that these time complexities represent the worst-case scenario, and actual running times may vary depending on factors such as input size and data distribution.