What are the patterns in coding?

In coding, patterns refer to recurring approaches and techniques used to solve common problems efficiently. These patterns help in simplifying problem-solving, optimizing algorithms, and reducing complexity in a structured manner. Here are some of the most important coding patterns:

1. Sliding Window Pattern

• Purpose: Useful for solving problems involving subarrays or substrings within a fixed-size window or when you need to slide over sequential data.
• Common Use Cases: Maximum sum of subarrays, longest substring without repeating characters, minimum window substring.

2. Two Pointers Pattern

• Purpose: This pattern is used when dealing with sorted arrays or linked lists and you need to find pairs of elements or check conditions from both ends.
• Common Use Cases: Pair sum problems, removing duplicates from a sorted array, and checking if a string is a palindrome.

3. Fast and Slow Pointers Pattern (Tortoise and Hare)

• Purpose: Typically used in problems involving cyclic data structures to detect cycles.
• Common Use Cases: Detecting a cycle in a linked list, finding the middle of a linked list.

4. Merge Intervals Pattern

• Purpose: Used to merge or manipulate overlapping intervals.
• Common Use Cases: Problems involving intervals, such as merging overlapping intervals or finding gaps between events.

5. Depth-First Search (DFS) / Breadth-First Search (BFS)

• Purpose: DFS and BFS are fundamental traversal algorithms for trees and graphs, used to explore nodes and edges in different ways.
• Common Use Cases: Solving mazes, finding all connected components, tree/graph traversal, shortest path problems.

6. Backtracking Pattern

• Purpose: Used for solving constraint satisfaction problems by exploring possible solutions and "backtracking" if a solution path does not work.
• Common Use Cases: Solving puzzles like Sudoku, N-Queens problem, and generating permutations and combinations.

7. Dynamic Programming (DP)

• Purpose: Breaks down problems into smaller, overlapping subproblems and stores the results of those subproblems to avoid redundant calculations.
• Common Use Cases: Fibonacci sequence, Knapsack problem, longest common subsequence.

8. Greedy Pattern

• Purpose: Involves making the best choice at each step in the hopes of finding the optimal solution. It is useful when local optimization leads to global optimization.
• Common Use Cases: Coin change problem, activity selection, and interval scheduling.

9. Topological Sort

• Purpose: Used to order tasks or vertices in a Directed Acyclic Graph (DAG) based on their dependencies.
• Common Use Cases: Course scheduling, task scheduling with dependencies.

10. Union-Find Pattern (Disjoint Set)

• Purpose: Efficiently manages and tracks a partition of a set into disjoint subsets.
• Common Use Cases: Network connectivity problems, cycle detection in graphs, Kruskal’s algorithm for finding the Minimum Spanning Tree (MST).

Conclusion

Coding patterns serve as efficient templates that guide you in solving complex problems. By learning patterns like Sliding Window, Dynamic Programming, and Greedy, you can speed up problem-solving and apply them to a wide range of algorithms. These patterns are critical for coding interviews and real-world applications.