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🧩 Intro to Algorithms Cheatsheets
Browse AI-generated Intro to Algorithms cheatsheets with one-page visual study aids for the key formulas, concepts, and definitions you need for your exam review.
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Unit 1 - 1.1 Definition and characteristics of algorithms
generated on 1/13/2026
Unit 1 - 1.2 Asymptotic notation and time complexity analysis
generated on 1/14/2026
Unit 1 - 1.3 Space complexity and algorithm efficiency
generated on 1/14/2026
Unit 1 - 1.4 Problem-solving strategies and algorithm design paradigms
generated on 1/14/2026
Unit 2 - 2.1 Arrays and dynamic arrays
generated on 1/14/2026
Unit 2 - 2.2 Singly and doubly linked lists
generated on 1/14/2026
Unit 2 - 2.3 Stacks and their applications
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Unit 2 - 2.4 Queues and priority queues
generated on 1/14/2026
Unit 3 - 3.1 Bubble Sort and its variations
generated on 1/14/2026
Unit 3 - 3.2 Selection Sort algorithm
generated on 1/14/2026
Unit 3 - 3.3 Insertion Sort and its applications
generated on 1/14/2026
Unit 3 - 3.4 Comparison of elementary sorting algorithms
generated on 1/14/2026
Unit 4 - 4.1 Divide-and-conquer paradigm
generated on 1/14/2026
Unit 4 - 4.2 Merge Sort algorithm and analysis
generated on 1/14/2026
Unit 4 - 4.3 Quick Sort algorithm and analysis
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Unit 4 - 4.4 Comparison of Merge Sort and Quick Sort
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Unit 5 - 5.1 Binary heap data structure
generated on 1/14/2026
Unit 5 - 5.2 Heap operations: insertion and deletion
generated on 1/14/2026
Unit 5 - 5.3 Heap Sort algorithm and analysis
generated on 1/9/2026
Unit 5 - 5.4 Priority queue implementations using heaps
generated on 1/14/2026
Unit 6 - 6.1 Hash table concept and basic operations
generated on 1/14/2026
Unit 6 - 6.2 Hash functions and collision resolution techniques
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Unit 6 - 6.3 Open addressing and chaining
generated on 1/14/2026
Unit 7 - 7.1 Binary search tree properties and operations
generated on 1/14/2026
Unit 7 - 7.2 Self-balancing trees: AVL trees
generated on 1/14/2026
Unit 7 - 7.3 Red-Black trees
generated on 1/9/2026
Unit 7 - 7.4 B-trees and their applications
generated on 1/14/2026
Unit 8 - 8.1 Graph terminology and representations
generated on 1/14/2026
Unit 8 - 8.2 Breadth-First Search (BFS) algorithm and applications
generated on 1/14/2026
Unit 8 - 8.3 Depth-First Search (DFS) algorithm and applications
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Unit 8 - 8.4 Comparison of BFS and DFS
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Unit 9 - 9.1 Minimum spanning tree concept
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Unit 9 - 9.2 Kruskal's algorithm and implementation
generated on 1/14/2026
Unit 9 - 9.3 Prim's algorithm and implementation
generated on 1/14/2026
Unit 9 - 9.4 Comparison and analysis of Kruskal's and Prim's algorithms
generated on 1/14/2026
Unit 10 - 10.1 Single-source shortest path problem
generated on 1/14/2026
Unit 10 - 10.2 Dijkstra's algorithm and implementation
generated on 1/14/2026
Unit 10 - 10.3 Bellman-Ford algorithm and negative edge weights
generated on 1/14/2026
Unit 10 - 10.4 Applications of shortest path algorithms
generated on 1/14/2026
Unit 11 - 11.1 Dynamic programming principles and methodology
generated on 1/14/2026
Unit 11 - 11.2 Fibonacci sequence and matrix chain multiplication
generated on 1/14/2026
Unit 11 - 11.3 Longest common subsequence and edit distance
generated on 1/14/2026
Unit 11 - 11.4 Knapsack problem and its variations
generated on 1/14/2026
Unit 12 - 12.1 Greedy algorithm paradigm and properties
generated on 1/14/2026
Unit 12 - 12.2 Interval scheduling problem and solutions
generated on 12/20/2025
Unit 12 - 12.3 Huffman coding algorithm and data compression
generated on 1/14/2026
Unit 13 - 13.1 Amortized analysis techniques
generated on 1/14/2026
Unit 13 - 13.2 Disjoint set data structure and union-find algorithms
generated on 1/14/2026
Unit 13 - 13.4 Splay trees and their amortized analysis
generated on 1/14/2026
Unit 14 - 14.1 P, NP, and NP-complete problem classes
generated on 1/14/2026
Unit 14 - 14.2 Cook's theorem and the SAT problem
generated on 1/4/2026
Unit 14 - 14.3 Reduction techniques and examples
generated on 1/14/2026
Unit 14 - 14.4 Approximation algorithms for NP-complete problems
generated on 1/14/2026
Unit 15 - 15.1 Approximation ratio and performance guarantees
generated on 1/14/2026
Unit 15 - 15.2 Vertex cover and set cover approximations
generated on 1/14/2026
Unit 15 - 15.3 Traveling salesman problem approximations
generated on 1/14/2026
Unit 15 - 15.4 Local search heuristics and metaheuristics
generated on 1/14/2026
Unit 16 - 16.1 Randomized algorithm design principles
generated on 1/14/2026
Unit 16 - 16.2 Las Vegas and Monte Carlo algorithms
generated on 1/14/2026
Unit 16 - 16.3 Randomized quicksort and selection algorithms
generated on 1/14/2026
Unit 16 - 16.4 Probabilistic analysis of algorithms
generated on 1/14/2026
about the Intro to Algorithms cheatsheets
A cheatsheet condenses a whole course into the facts you actually need on exam day. The Intro to Algorithms cheatsheets on this page summarize the key formulas, concepts, and definitions from each unit so you can review the entire course at a glance.
They work best as a final review: skim the one-page summary, spot the units you want to strengthen, then go deeper with the Intro to Algorithms study guides. The visual cheatsheets in the gallery are organized by unit, and you can generate your own for any topic you want a quick refresher on.