Coding interview patterns are strategies or approaches used to solve common types of problems encountered in technical interviews. Familiarizing yourself with these patterns can significantly enhance your problem-solving efficiency. Here are some key patterns often seen in coding interviews:

1. Sliding Window

• Used For: Problems involving contiguous subarrays or substrings, like finding the longest substring with no repeating characters.
• Key Concept: Dynamically adjust the start and end of a window to satisfy certain conditions.

2. Two Pointers

• Used For: Array and linked list problems, such as reversing an array or finding a pair that sums up to a target.
• Key Concept: Using two pointers, which could move towards each other or in the same direction, to efficiently solve problems without extra space.

3. Fast and Slow Pointers

• Used For: Detecting cycles in a linked list, finding the middle element of a list.
• Key Concept: Two pointers moving at different speeds to solve problems in a single pass.

4. Divide and Conquer

• Used For: Complex problems that can be broken down into smaller sub-problems, like merge sort or quick sort.
• Key Concept: Divide the problem into sub-problems, solve them independently, and then combine the results.

5. Dynamic Programming

• Used For: Problems requiring optimization over time, like finding the longest increasing subsequence or the minimum path sum.
• Key Concept: Storing the results of sub-problems to avoid redundant work.

6. Backtracking

• Used For: Problems where you need to find all possible solutions, like permutations of a string or solving a Sudoku.
• Key Concept: Explore each possibility and backtrack to try a different path if a dead end is reached.

7. Breadth-First Search (BFS)

• Used For: Traversing trees or graphs level by level, like finding the shortest path in a maze.
• Key Concept: Use a queue to process nodes level by level.

8. Depth-First Search (DFS)

• Used For: Exploring all paths or combinations in a tree or graph, like finding all leaf nodes.
• Key Concept: Use recursion or a stack to explore paths to their fullest before backtracking.

9. Greedy Algorithms

• Used For: Problems where local optimization leads to a global solution, like finding the minimum number of coins for change.
• Key Concept: Choose the best option at the current moment without considering the bigger picture.

10. Binary Search

• Used For: Searching in a sorted array or finding an element satisfying certain conditions.
• Key Concept: Repeatedly divide the search interval in half to find the target.

11. Segment Trees

• Used For: Problems requiring range queries and modifications over an array, like finding the sum or minimum in a range.
• Key Concept: A tree data structure that allows storing information about array segments, enabling efficient queries and updates.

12. Topological Sort (Graphs)

• Used For: Problems involving dependencies, like course scheduling or build system ordering.
• Key Concept: Sort nodes in a directed graph in a linear order where for every directed edge from node A to B, A comes before B.

13. Union-Find (Disjoint Set)

• Used For: Problems involving grouping or connecting components, like finding connected components in a graph.
• Key Concept: A data structure that keeps track of elements partitioned into disjoint sets and supports union and find operations.

14. Interval Merging

• Used For: Problems involving overlapping intervals, like merging meeting times.
• Key Concept: Merge overlapping intervals to produce a set of mutually exclusive intervals.

15. Heap (Priority Queue)

• Used For: Problems requiring constant access to the minimum or maximum element, like finding the Kth largest element.
• Key Concept: A binary tree-based data structure that maintains the heap property (min-heap or max-heap).

16. Trie (Prefix Tree)

• Used For: Problems involving string manipulation, like autocomplete features or word searches.
• Key Concept: A tree-like data structure that stores a dynamic set of strings where each node represents a character of a string.

17. Kadane's Algorithm (Dynamic Programming)

• Used For: Finding the maximum sum subarray in an array.
• Key Concept: Dynamic programming approach to keep track of the maximum sum of subarrays ending at different positions.

18. Floyd's Tortoise and Hare (Cycle Detection)

• Used For: Detecting cycles in a sequence or linked list.
• Key Concept: Use two pointers moving at different speeds to determine whether a cycle exists.

19. Suffix Array and Suffix Tree

• Used For: Advanced string problems, like finding the longest repeated substring.
• Key Concept: Data structures that provide efficient ways to handle queries related to substrings. Suffix arrays are a space-efficient version of suffix trees.

20. Bucket Sort / Counting Sort

• Used For: Sorting problems where the input is uniformly distributed over a range.
• Key Concept: Sort elements by distributing them into a number of buckets and then sorting these buckets individually.

Conclusion

Each of these patterns offers a unique approach to solving complex problems and can be particularly effective for certain types of questions in coding interviews. Understanding and practicing these patterns can significantly enhance your problem-solving skills and improve your performance in technical interviews.