Greedy SLIM

Greedy algorithms, characterized by their iterative, locally optimal choices, are being explored across diverse machine learning applications. Current research focuses on improving their efficiency and effectiveness in areas such as recommender systems (Greedy SLIM), large language model inference acceleration ("stairs" assisted greedy generation), and vision graph neural networks (GreedyViG), often demonstrating significant performance gains compared to alternative approaches. This focus on greedy methods stems from their computational advantages, particularly in high-dimensional or real-time settings, offering a valuable trade-off between computational cost and solution quality across various domains.