High Dimensional Tensor

High-dimensional tensors, multi-dimensional arrays extending matrices, are central to analyzing complex, multi-faceted data. Current research focuses on developing efficient algorithms for tensor decomposition and optimization, particularly within models like Tucker factorization and tensor trains, to address challenges like high dimensionality and noise. These methods are applied across diverse fields, including computer vision, material science, and data mining, enabling improved accuracy and efficiency in tasks such as image processing, atomistic simulations, and passenger flow modeling. The development of robust and scalable algorithms for high-dimensional tensor analysis is crucial for advancing numerous scientific and engineering disciplines.