Optimizing AI Computation Using Infinite-Dimensional Mathematics

This study, based on the theory of infinite-dimensional mathematics, explores how to use the exponential trends of infinity and infinitesimals to optimize complex computational problems, particularly in AI computation and mathematical physics modeling. We propose a novel approach that maps the growth trend of computational problems to mathematical expressions involving exponentials and roots of infinity and infinitesimals. By leveraging these properties, our method provides computational optimization that reduces exponential computational expansion and improves efficiency. The paper presents specific mathematical models and derivations, demonstrating how this approach can be applied to complex computational environments. This research offers a new mathematical tool for AI computation optimization, the P vs NP problem, and mathematical physics predictions, showcasing the potential applications of infinite-dimensional mathematics.