Leetcode Coding Patterns

1. Two Pointers

   • The Two Pointers pattern involves using two pointers to iterate through an array or list, often used to find pairs or elements that meet specific criteria.

2. Prefix Sum

   • Prefix Sum involves preprocessing an array to create a new array where each element at index i represents the sum of the array from the start up to i. This allows for efficient sum queries on subarrays.

3. Sliding Window

   • The Sliding Window pattern is used to find a subarray or substring that satisfies a specific condition, optimizing the time complexity by maintaining a window of elements.

4. Slow Fast Pointers

   • The Fast & Slow Pointers (Tortoise and Hare) pattern is used to detect cycles in linked lists and other similar structures.

5. Top K Element

   • The Top 'K' Elements pattern finds the top k largest or smallest elements in an array or stream of data using heaps or sorting.

6. Overlapping Intervals

   • The Overlapping Intervals pattern is used to merge or handle overlapping intervals in an array.

7. Bit Manipulation

8. Modified Binary Search

   • The Modified Binary Search pattern adapts binary search to solve a wider range of problems, such as finding elements in rotated sorted arrays.
   • Use this pattern for problems involving sorted or rotated arrays where you need to find a specific element.

9. Monotonic Stack

   • The Monotonic Stack pattern uses a stack to maintain a sequence of elements in a specific order (increasing or decreasing).
   • Use this pattern for problems that require finding the next greater or smaller element.

10. Monotonic Queue

    • The Monotonic Queue pattern uses a queue to maintain a sequence of elements in a specific order (increasing or decreasing).

11. LinkedList In-place Reversal

    • The In-place Reversal of a LinkedList pattern reverses parts of a linked list without using extra space.

12. Two Heaps

    • This approach is quite useful when dealing with the problems where we are given a set of elements such that we can divide them into two parts.

13. Dynamic Programming

    • Dynamic Programming (DP) involves breaking down problems into smaller subproblems and solving them using a bottom-up or top-down approach.

    • Use this pattern for problems with overlapping subproblems and optimal substructure.