基于自适应模型的非线性张量变换

This paper introduces a novel algorithm for non-linear tensor transformations, leveraging a dynamically adaptive model to optimize computation. Traditional tensor transformations often rely on fixed parameters, limiting their efficiency, particularly when dealing with large datasets. Our proposed method employs an adaptive model that adjusts transformation parameters during the computation process, resulting in reduced computational complexity and improved performance. We provide a detailed explanation of the algorithm's core mechanisms, demonstrating its effectiveness through theoretical analysis and preliminary experimental results. This research addresses a critical need for efficient and adaptable tensor manipulation in various scientific and engineering applications.