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Algorithm Patterns

Mastering algorithmic patterns is essential for solving coding challenges efficiently. This section covers the most common patterns you'll encounter in technical interviews and real-world development.

What Are Algorithm Patterns?

Algorithm patterns are reusable approaches to solving specific types of problems. Instead of memorizing individual solutions, learning these patterns helps you:

  • Recognize problem types quickly
  • Apply the right technique systematically
  • Adapt solutions to similar problems
  • Build problem-solving intuition

Core Patterns

Sliding Window

Optimizes problems involving contiguous sequences (subarrays, substrings). Perfect for finding optimal windows or tracking elements within a range.

Common Use Cases: Maximum/minimum subarray sum, longest substring problems, fixed-size window aggregations

Dynamic Programming

Breaks complex problems into overlapping subproblems, storing results to avoid redundant calculations.

Common Use Cases: Optimization problems, counting paths, sequence alignment, knapsack variations

Binary Search on Answer

Uses binary search to efficiently find optimal values by testing candidate solutions.

Common Use Cases: Finding minimum/maximum values meeting constraints, capacity problems, resource allocation

Two Pointers

Uses multiple pointers to traverse data structures, often from different positions or directions.

Common Use Cases: Pair finding, array partitioning, merging sorted arrays, palindrome checking

Fast and Slow Pointers

Uses pointers moving at different speeds to detect cycles or find specific positions.

Common Use Cases: Cycle detection, finding middle elements, linked list problems

Backtracking

Systematically explores all possible solutions by building candidates incrementally and abandoning them when they fail to meet constraints.

Common Use Cases: Permutations, combinations, constraint satisfaction, puzzle solving

Heap and Priority Queue

Maintains elements in sorted order for efficient access to minimum or maximum. Essential for problems requiring frequent extreme value access.

Common Use Cases: K-th largest/smallest, merge K lists, task scheduling, running median

Monotonic Stack

Maintains stack elements in monotonic order to efficiently find next/previous greater or smaller elements.

Common Use Cases: Next greater element, histogram problems, temperature problems, range queries

Graph Traversal (BFS/DFS)

Systematic graph exploration using breadth-first or depth-first search. Foundation for many graph algorithms.

Common Use Cases: Shortest path, connected components, cycle detection, level-order traversal

Greedy Algorithms

Makes locally optimal choices at each step to find global optimum. Fast but requires proof of correctness.

Common Use Cases: Activity selection, scheduling, interval problems, optimization with specific properties

Trie (Prefix Tree)

Tree structure for efficient string prefix operations. Each path represents a word with shared prefixes.

Common Use Cases: Autocomplete, spell check, prefix matching, dictionary operations, word games

How to Use This Guide

Each pattern section includes:

  1. Conceptual Overview - Understanding when and why to use the pattern
  2. Step-by-Step Approach - How to apply the pattern systematically
  3. Three LeetCode Examples - Real problems with complete solutions
  4. Implementation Details - Code walkthroughs with explanations

Start with a pattern that interests you, work through the examples, and practice applying the approach to similar problems.