Algorithmic Patterns

1. Two Pointers

• A technique to solve problems is having two pointers starting at different positions and moving towards a condition.
• Useful for problems like finding a pair in a sorted array, or reversing a string in place.

2. Island

• Used for solving matrix traversal problems like counting the number of islands in a grid.
• Often solved using DFS or BFS.

3. Fast and Slow Pointer

• Also known as the "Tortoise and Hare" approach.
• Commonly used to detect cycles in a linked list or to find the middle of the list.

4. Sliding Window

• Useful for problems involving subarrays of a fixed size or maximum/minimum sum problems.
• Efficiently solve problems by sliding a window over the data.

5. Merge Intervals

• Merging overlapping intervals, typically used in scheduling and range-related problems.
• Often solved using sorting.
• https://www.youtube.com/playlist?list=PLamEquLLzOthgVHWr9GwyDFEHyxudZRtf
• LeetCode 56: Merge Intervals
• LeetCode 1288: Remove Covered Intervals

6. Cyclic Sort

• Sorting an array of numbers when the numbers are in a specific range.
• Used for problems that involve finding missing or duplicate numbers.

7. In-place Reversal of a Linked List

• Reversing a linked list using constant space.
• Interview questions involving linked lists are commonly asked.

8. Tree Breadth-First Search

• Traversing a tree level by level.
• Useful for problems involving the shortest path in an unweighted graph.

9. Tree Depth First Search

• Traversing a tree by going deep along one branch before moving to another.
• Useful for problems like finding paths, tree diameter, etc.

10. Two Heaps

• Technique for efficiently keeping track of the median of a stream of numbers.
• Uses a max heap and a min-heap.

11. Subsets

• Finding all possible subsets of a given set.
• Can be solved using backtracking or iterative approaches.

12. Modified Binary Search

• Binary search with variations to solve non-trivial searching problems.
• Examples include finding rotation points or closest elements in a sorted array.

13. Top 'K' elements

• Finding the top K elements in a collection.
• Can be solved efficiently using heaps or quickselect.

14. Bitwise XOR

• Bit manipulation technique.
• Useful for problems like finding missing numbers in an array.

15. Backtracking

• Try all possible solutions and backtrack once an invalid solution is reached.
• Commonly used in solving problems like Sudoku, N-Queens, etc.

16. 0/1 Knapsack

• A dynamic programming problem involving maximizing value without exceeding weight.
• Uses a table to store intermediate results.

17. Topological Sort

• Sorting directed graphs with dependencies.
• Used in problems involving course scheduling or task ordering.

18. K-way Merge

• Merging K sorted lists into a single sorted list.
• Uses a min-heap for efficient merging.

19. Monotonic Stack

• Stack used to maintain elements in a sorted order.
• Useful for solving problems involving the next greater or smaller element.

20. Multi Thread

• Concurrently executing multiple threads for parallel computation.
• Helps in optimizing performance in multi-core systems.