## 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. **Dijkstraâ€™s Algorithm**

**Explanation**: Dijkstraâ€™s finds the shortest path in a weighted graph with non-negative edge weights.**LeetCode Practice**: Dijkstraâ€™s Algorithm Practice Problems

## 10. **Kruskalâ€™s Algorithm**

**Explanation**: Kruskalâ€™s constructs a minimum spanning tree by adding edges in ascending order of weight.**LeetCode Practice**: Kruskalâ€™s Algorithm Practice Problems

Certainly! Letâ€™s continue our journey through the remaining algorithms, diving deeper into their explanations, providing references, and suggesting LeetCode problems for practice. ðŸš€

## 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 doesnâ€™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! Letâ€™s continue exploring the remaining algorithms, along with explanations and LeetCode problems for each. ðŸš€

## 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. Itâ€™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. Itâ€™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. **Floydâ€™s Cycle Detection Algorithm**

**Explanation**: Floydâ€™s algorithm detects cycles in linked lists by using two pointersâ€”one slow and one fastâ€”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

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