Here is a table summarizing the algorithms along with their best-case, average-case, and worst-case time complexities:
| Algorithm | Best Case | Average Case | Worst Case |
|---|---|---|---|
| Selection Sort | Ω(n^2) | Θ(n^2) | O(n^2) |
| Insertion Sort | Ω(n) | Θ(n^2) | O(n^2) |
| Merge Sort | Ω(n log n) | Θ(n log n) | O(n log n) |
| Quick Sort | Ω(n log n) | Θ(n log n) | O(n^2) |
| Binary Search | Ω(1) | Θ(log n) | O(log n) |
| Strassen's Multiplication | Ω(n^2.8074) | Θ(n^2.8074) | O(n^2.8074) |
| Matrix Chain Multiplication | Ω(n^3) | Θ(n^3) | O(n^3) |
| Longest Common Subsequence | Ω(m * n) | Θ(m * n) | O(m * n) |
| Job Sequencing (Greedy) | Ω(n log n) | Θ(n log n) | O(n^2) |
| Dijkstra's Algorithm | Ω((V + E)logV) | Θ(V + ElogV) | O(V^2) |
| Prim's Algorithm | Ω(E log V) | Θ(E log V) | O(E log V) |
| Kruskal's Algorithm | Ω(E log E) | Θ(E log E) | O(E log E) |
| Fractional Knapsack (Greedy) | Ω(n log n) | Θ(n log n) | O(n log n) |
| N-Queen Problem (Backtracking) | Ω(n!) | Θ(n!) | O(n!) |
| Subset Sum (Backtracking) | Ω(2^n) | Θ(2^n) | O(2^n) |
| Graph Coloring | Ω(m^V) | Θ(m^V) | O(m^V) |
| 0/1 Knapsack (Branch & Bound) | Ω(n * W) | Θ(n * W) | O(n * W) |
| Traveling Salesman (Branch & Bound) | Ω(n^2 * 2^n) | Θ(n^2 * 2^n) | O(n^2 * 2^n) |
| 15 Puzzle Problem | Ω(b^d) | Θ(b^d) | O((n! * log n!) |
| Vertex Cover | Ω(V + E) | Θ(V + E) | O(V + E) |
| Set Cover (Approximation) | Ω(2^n) | Θ(2^n) | O(2^n) |
| Rabin-Karp Algorithm | Ω(n + m) | Θ(n + m) | O(n * m) |
| Knuth-Morris-Pratt Algorithm | Ω(n + m) | Θ(n + m) | O(n + m) |
Please note that these are just asymptotic complexities, and the actual running time of an algorithm may vary depending on the input data.