1.11 Best Worst and Average Case Analysis | Highlights and Annotations by Gistr.

This segment focuses on the binary search tree, explaining its structure and how a search operation works. It then defines the best-case scenario for searching in a binary search tree, where the target element is found at the root node, resulting in a constant time complexity. This video explains best, worst, and average case analysis of algorithms using linear search and binary search tree examples. Linear search's best case is O(1), worst case is O(n), and average case is approximately O(n). Binary search tree's best case is O(1), worst case is O(n) (for unbalanced trees) and O(log n) (for balanced trees). The video emphasizes that Big O, Big Omega, and Big Theta notations can be applied to all cases, not just worst-case. This segment provides a comprehensive overview of best-case, worst-case, and average-case time complexities for linear search. It clearly defines each case and explains how the time complexity differs depending on the position of the target element within the list. This segment delves into the worst-case time complexity of searching in a binary search tree. It explains how the worst-case scenario depends on the height of the tree, which can vary depending on the tree's structure. The explanation covers both minimum and maximum worst-case times.