Complexity Change

Complexity change research investigates how the difficulty of computational problems scales with input size and problem parameters. Current efforts focus on analyzing complexity in diverse areas, including logical inference, event prediction (using machine learning and analytical methods), and optimization problems within probability spaces (e.g., variational inference). These studies often involve developing novel algorithms with improved complexity bounds or characterizing the relationship between problem complexity and data requirements for successful model training. Understanding and mitigating complexity is crucial for advancing the efficiency and scalability of numerous algorithms across various scientific and engineering disciplines.