Complexity Analysis

Complexity analysis investigates the computational resources required by algorithms and models, focusing on efficiency and scalability. Current research explores this across diverse fields, including analyzing the performance limitations of large language models in inductive learning, evaluating the computational complexity of various algorithms in machine learning (e.g., tree averaging, complex-valued neural networks), and examining the complexity of problems in multi-agent systems and game theory. These analyses are crucial for developing efficient and practical algorithms, improving model design, and understanding the fundamental limits of computation in various domains.