Algorithm Reference
Eigenvue includes 22 algorithms across four categories: Classical Algorithms, Deep Learning, Generative AI, and Quantum Computing. Each algorithm provides step-by-step visual explanations, interactive playback controls, and shareable deep links. Click any algorithm name below to view its detailed documentation, including input options, step format, and educational content.
Classical Algorithms
Section titled “Classical Algorithms”Foundational algorithms for searching, sorting, and graph traversal. These form the basis of computer science education and are essential building blocks for more advanced topics.
| Algorithm | Difficulty | Time Complexity | Space Complexity |
|---|---|---|---|
| Binary Search | Beginner | O(log n) | O(1) |
| Bubble Sort | Beginner | O(n²) | O(1) |
| QuickSort | Intermediate | O(n log n) avg | O(log n) |
| Merge Sort | Intermediate | O(n log n) | O(n) |
| BFS | Intermediate | O(V + E) | O(V) |
| DFS | Intermediate | O(V + E) | O(V) |
| Dijkstra’s | Advanced | O((V + E) log V) | O(V) |
Deep Learning
Section titled “Deep Learning”Core building blocks of neural networks, from single neurons to gradient-based optimization. These visualizations help build intuition for how deep learning models learn.
| Algorithm | Difficulty | Key Concept |
|---|---|---|
| Perceptron | Beginner | Single neuron, activation functions |
| Feedforward Network | Intermediate | Multi-layer signal propagation |
| Backpropagation | Intermediate | Gradient computation, chain rule |
| Convolution | Intermediate | Kernel sliding, feature maps |
| Gradient Descent | Intermediate | Optimization, loss landscapes |
Generative AI
Section titled “Generative AI”Components of modern generative AI architectures, focusing on the transformer pipeline from raw text to contextualized representations.
| Algorithm | Difficulty | Key Concept |
|---|---|---|
| Tokenization (BPE) | Beginner | Byte-pair encoding, vocabulary |
| Token Embeddings | Intermediate | Vector representations, similarity |
| Self-Attention | Intermediate | Q/K/V projections, attention weights |
| Multi-Head Attention | Advanced | Multiple heads, concatenation |
| Transformer Block | Advanced | Full block: attention + FFN + residual |
Quantum Computing
Section titled “Quantum Computing”Fundamental quantum computing concepts, from single-qubit states on the Bloch sphere to multi-qubit algorithms like Grover’s search and quantum teleportation.
| Algorithm | Difficulty | Key Concept |
|---|---|---|
| Qubit Bloch Sphere | Beginner | Qubit states, Bloch sphere geometry |
| Quantum Gates | Intermediate | Unitary transformations, gate circuits |
| Superposition & Measurement | Intermediate | Born rule, state collapse |
| Grover’s Search | Advanced | Oracle, diffusion, quadratic speedup |
| Quantum Teleportation | Advanced | Bell pairs, classical communication |