Top 7 Algorithms for Coding Interviews

0:00 Intro

The video highlights the top 7 algorithms crucial for coding interviews, divided into three search algorithms, three sorting algorithms, and a greedy algorithm. Understanding basic data structures and time complexity is recommended before diving into these algorithms.

0:57 Binary Search

Binary search efficiently finds an element's position in a sorted list by repeatedly dividing the search range in half. It significantly reduces the number of guesses needed compared to linear search, which has a runtime of O(n), while binary search operates at O(log n).

3:43 Depth-First Search

Depth-first search (DFS) explores the deepest nodes first before backtracking to find unvisited branches. This method is useful for traversing trees and graphs, with a time complexity of O(V + E), where V is vertices and E is edges.

6:39 Breadth-First Search

Breadth-first search (BFS) examines all nodes at the present depth before moving on to nodes at the next level. It also has a time complexity of O(V + E) and is often used in applications like chess algorithms.

9:13 Insertion Sort

Insertion sort builds a sorted list by comparing adjacent elements and swapping them if they are in the wrong order. It has a best-case runtime of O(n) and a worst-case of O(n²), making it effective for small or mostly sorted lists.

10:57 Merge Sort

Merge sort is a divide-and-conquer algorithm that splits the list into smaller parts, sorts them, and merges them back together. It maintains a time complexity of O(n log n) for both the best and worst cases, making it suitable for larger lists.

12:52 Quick Sort

Quick sort, another divide-and-conquer algorithm, selects a pivot and partitions the list into elements less than and greater than the pivot. It has a best-case runtime of O(n log n) and a worst-case of O(n²), but is typically faster than merge sort on average.

15:57 Greedy Algorithms

Greedy algorithms make the best local choice at each step without considering future consequences. They are useful when exact optimization is impossible, as illustrated by the traveling salesman problem, where they provide a practical but not optimal solution.