Must Know Algorithms.

1. Searching Algorithms

• Linear Search: Iteratively searches each element in the array.
  
  Time Complexity: O(n)




• Binary Search: Compares the target with the middle element of a sorted array and narrows down the search.
  
  Time Complexity: O(log₂n)

2. Sorting Algorithms

• Bubble Sort: Swaps adjacent elements in repeated passes.
  
  Time Complexity: O(n²)




• Insertion Sort: Inserts elements into their correct position in a sorted part of the array.
  
  Time Complexity: O(n²)




• Selection Sort: Selects the smallest value from unsorted elements in each pass.
  
  Time Complexity: O(n²)




• Heap Sort: Uses heaps to sort elements.
  
  Time Complexity: O(n log n)




• Merge Sort: A divide-and-conquer algorithm that divides the array, sorts each half, and merges.
  
  Time Complexity: O(n log n)




• Quick Sort: Uses a pivot to partition and recursively sort arrays.
  
  Time Complexity: O(n log n) (average), O(n²) (worst case).

3. Basic Math Algorithms

• 
  Euclid's Algorithm for GCD: Finds the greatest common divisor by division.


• 
  Sieve of Eratosthenes: Identifies prime numbers by eliminating multiples.


• 
  Bit Manipulations: Uses bitwise operators for low-level operations.

4. Graph Algorithms

• 
  Breadth-First Search (BFS): Traverses level by level using a queue.


• 
  Depth-First Search (DFS): Explores depth-wise using a stack.
  Time Complexity: O(V + E)


• D*ijkstra's Algorithm:* Finds the shortest path in a weighted graph.

5. Tree Algorithms

• 
  Inorder Traversal: Left subtree → Root → Right subtree.


• 
  Preorder Traversal: Root → Left subtree → Right subtree.


• 
  Postorder Traversal: Left subtree → Right subtree → Root.
  Time Complexity: O(n)


• 
  Kruskal's Algorithm: Finds the minimum spanning tree by adding edges in order of weight.

6. Dynamic Programming

• 
  Floyd-Warshall Algorithm: Finds shortest paths between all pairs in a weighted graph. Uses Memoization (top-down) and Tabulation (bottom-up).

7. Backtracking Algorithms

• Solves problems like N-Queens, Sum of Subsets, Graph Coloring, and Hamiltonian Cycles.

8. Huffman Compression Algorithm

• Compresses data by building a Huffman tree and assigning codes to characters based on frequency.