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Algorithmic Patterns

Introduction

Memorizing solutions to 500 LeetCode problems is impossible and ineffective. Instead, you should memorize Algorithmic Patterns.

Once you recognize the underlying pattern of a problem, the solution becomes a matter of applying the standard template and making minor adjustments for the specific edge cases.

What You Need to Know

In this section, we will cover the core algorithmic patterns that appear in 90% of coding interviews. For each pattern, we will provide a theoretical overview, the Pythonic way to implement it, and the types of questions that signal you should use it.

We cover: - Array Patterns: Sliding Window, Two Pointers, Prefix Sum, Difference Array. - Stack & Queue: Monotonic Stacks, Monotonic Queues. - Tree & Graph: DFS, BFS, Topological Sort, Union-Find. - Advanced: Dynamic Programming, Greedy Algorithms, Bit Manipulation.

Key Concept: Pattern Recognition

During an interview, listen carefully to the problem description constraints. They are hints pointing you toward specific patterns.

  • "Sorted array" -> Two Pointers or Binary Search
  • "Contiguous subarray" -> Sliding Window or Prefix Sum
  • "Tree/Graph shortest path" -> BFS
  • "All permutations/combinations" -> Backtracking
  • "Maximum/Minimum overlapping intervals" -> Sorting + Sweepline/Heaps
  • "Top K elements" -> Heaps
  • "Next greater element" -> Monotonic Stack

Mastering these mappings is the key to passing technical interviews.