Query Complexity

Query complexity, the number of queries needed to solve a computational problem, is a central theme in algorithm design, focusing on minimizing resource usage while maintaining solution quality. Current research emphasizes efficient algorithms for diverse problems, including submodular maximization (often employing greedy or thresholding techniques), active learning (exploring near-orthogonal basis functions), and query answering over knowledge graphs (leveraging graph neural networks). Understanding and optimizing query complexity is crucial for improving the efficiency and scalability of machine learning, data management, and optimization algorithms across various applications.