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25 Must-Know Algorithms for Coding Interviews

1. Bubble Sort

  • Explanation: Bubble Sort is a simple sorting method where adjacent elements are swapped if they are in the incorrect order.
  • Time Complexity: O(n^2)
  • LeetCode Practice: Bubble Sort Practice Problems

2. Counting Sort

  • Explanation: Counting Sort counts the frequency of each element and replaces it with its frequency.
  • Time Complexity: O(n)
  • LeetCode Practice: Not directly applicable for sorting, but useful for other problems.

3. Merge Sort

  • Explanation: Merge Sort divides the array into smaller components, sorts them, and then merges them back together.
  • Time Complexity: O(n log n)
  • LeetCode Practice: Merge Sort Practice Problems

4. QuickSort

  • Explanation: QuickSort is based on divide and conquer. It selects a pivot and partitions the list into smaller and greater elements.
  • Time Complexity: O(n log n)
  • LeetCode Practice: QuickSort Practice Problems

5. Binary Search

  • Explanation: Binary Search finds the position of an element in a sorted array by dividing the search space in half.
  • Time Complexity: O(log n)
  • LeetCode Practice: Binary Search Practice Problems

6. Insertion Sort

  • Explanation: Insertion Sort compares adjacent elements and swaps them until the array is sorted.
  • Time Complexity: O(n^2)
  • LeetCode Practice: Insertion Sort Practice Problems

7. Depth-First Search (DFS)

  • Explanation: DFS explores a graph or tree by visiting nodes as deeply as possible before backtracking.
  • LeetCode Practice: DFS Practice Problems

8. Breadth-First Search (BFS)

  • Explanation: BFS explores a graph level by level, visiting neighbors before deeper nodes.
  • LeetCode Practice: BFS Practice Problems

9. Dijkstra’s Algorithm

  • Explanation: Dijkstra’s finds the shortest path in a weighted graph with non-negative edge weights.
  • LeetCode Practice: Dijkstra’s Algorithm Practice Problems

10. Kruskal’s Algorithm

  • Explanation: Kruskal’s constructs a minimum spanning tree by adding edges in ascending order of weight.
  • LeetCode Practice: Kruskal’s Algorithm Practice Problems

Certainly! Let’s continue our journey through the remaining algorithms, diving deeper into their explanations, providing references, and suggesting LeetCode problems for practice. 🚀

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11. Knuth-Morris-Pratt Algorithm

  • Explanation: The Knuth-Morris-Pratt (KMP) algorithm efficiently searches for a pattern within a given text by avoiding unnecessary character comparisons.
  • Time Complexity: O(n + m), where n is the length of the text and m is the length of the pattern.
  • LeetCode Practice: Implement strStr(), Shortest Palindrome

12. Boyer-Moore Algorithm

  • Explanation: The Boyer-Moore algorithm is a powerful string-searching technique that skips characters based on precomputed tables.
  • Time Complexity: O(n + m), where n is the length of the text and m is the length of the pattern.
  • LeetCode Practice: Implement strStr(), Majority Element II

13. Traveling Salesman Problem (TSP) Algorithms

  • Explanation: The Traveling Salesman Problem aims to find the shortest route that visits a set of cities and returns to the starting city.
  • LeetCode Practice: TSP is a classic optimization problem, and LeetCode doesn’t have direct TSP problems. However, you can adapt other problems to TSP-like scenarios.

14. Longest Common Subsequence (LCS) Algorithms

  • Explanation: LCS algorithms find the longest subsequence common to two or more sequences (usually strings).
  • Time Complexity: O(m * n), where m and n are the lengths of the input sequences.
  • LeetCode Practice: Longest Common Subsequence, Edit Distance

15. Minimum Spanning Tree (MST) Algorithms

  • Explanation: MST algorithms find the minimum-weight tree that spans all vertices in a connected graph.
  • LeetCode Practice: MST problems are not directly available on LeetCode, but you can adapt other graph problems to MST scenarios.

Certainly! Let’s continue exploring the remaining algorithms, along with explanations and LeetCode problems for each. 🚀

16. Dynamic Programming

  • Explanation: Dynamic Programming (DP) optimizes recursive algorithms by storing and reusing intermediate results. It breaks down complex problems into simpler subproblems and builds solutions incrementally.
  • LeetCode Practice: Dynamic Programming Practice Problems

17. Greedy Algorithms

  • Explanation: Greedy algorithms make locally optimal choices at each step to find a global optimum solution. They work well for certain problems like the fractional knapsack problem and coin change.
  • LeetCode Practice: Greedy Algorithms Practice Problems

18. Backtracking Algorithms

  • Explanation: Backtracking explores all possible solutions by incrementally building candidates and backtracking when a partial solution cannot lead to a valid complete solution. It’s commonly used for combination and permutation problems.
  • LeetCode Practice: Backtracking Practice Problems

19. Divide and Conquer Algorithms

  • Explanation: Divide and Conquer partitions problems into disjoint subproblems, solves them recursively, and then combines the solutions to solve the original problem. It’s useful for problems like merge sort and finding topological orderings.
  • LeetCode Practice: Divide and Conquer Practice Problems

20. Topological Sorting

  • Explanation: Topological sorting orders vertices in a directed acyclic graph (DAG) such that for every directed edge UV, vertex U comes before V in the ordering.
  • LeetCode Practice: Topological Sorting Practice Problems

21. Floyd’s Cycle Detection Algorithm

  • Explanation: Floyd’s algorithm detects cycles in linked lists by using two pointers—one slow and one fast—to traverse the list.
  • LeetCode Practice: Linked List Cycle, Linked List Cycle II

22. K-means Clustering

  • Explanation: K-means clustering partitions data points into K clusters based on similarity.
  • LeetCode Practice: K-means is not directly available on LeetCode, but you can adapt other problems to K-means scenarios.

23. Expectation-Maximization (EM) Algorithm

  • Explanation: EM algorithm estimates parameters of statistical models when data is incomplete or has missing values.
  • LeetCode Practice: EM is not directly available on LeetCode, but you can adapt other problems to EM scenarios.

24. Binary Search

  • Explanation: Binary search efficiently finds a target value in a sorted array by dividing the search space in half.
  • LeetCode Practice: Binary Search Practice Problems

25. Quick Sort

  • Explanation: Quick sort is a divide-and-conquer algorithm that selects a pivot and partitions the list into smaller and greater elements.
  • LeetCode Practice: Quick Sort Practice Problems

Feel free to bookmark this blog and share it with your friends who are preparing for coding interviews. Happy coding! 😊

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Gulshan
Gulshanhttps://noticedash.com
Hi, I am Gulshan Yadav. I work at NoticeDash.com as a Chief Editor.

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