Lec-7 : Time & Space Complexity Analysis of BFS & DFS In Artificial Intelligence with Examples скачать в хорошем качестве

Lec-7 : Time & Space Complexity Analysis of BFS & DFS In Artificial Intelligence with Examples

In this video, we provide an in-depth analysis of the Time and Space Complexity for two fundamental AI search algorithms: Breadth-First Search (BFS) and Depth-First Search (DFS). Using a practical example of a tree with a branching factor (b) of 2 and a depth (d) of 3, we break down the mathematical logic behind these complexities,. Key Topics Covered: • Breadth-First Search (BFS): ◦ Exploration method: Level-by-level. ◦ Data Structure: Queue (FIFO). ◦ Time Complexity: O(b^d) because every node up to the goal depth must be explored,. ◦ Space Complexity: O(b^d) as the algorithm must store all nodes of the current level in the queue. • Depth-First Search (DFS): ◦ Exploration method: Depth-wise with backtracking,. ◦ Data Structure: Stack (LIFO). ◦ Time Complexity: O(b^d), similar to BFS in the worst-case scenario,. ◦ Space Complexity: O(b×d) (or linear relative to depth), making it significantly more space-efficient than BFS because it only stores nodes along the current path. We also walk through the Geometric Progression (GP) series formula used to calculate the total number of nodes explored in BFS. Whether you are preparing for university exams or competitive exams like GATE, this video will clear all your doubts regarding search algorithm efficiency. -------------------------------------------------------------------------------- Hashtags #artificialintelligence #bfs #dfs #timecomplexity #SpaceComplexity #algorithms #computerscience #searchalgorithms #datastructures #gatepreparation #ai #bilalofficial_21