An optimization approach for transformed low tubal rank of third-order tensors

Key Points

• The novel optimization approach enhances the understanding of transformed low tubal rank in third-order tensors, improving efficiency.
• Key metrics indicate significant computational advantages, enabling better tensor decompositions in complex datasets.
• Assessing transformed tensors through advanced optimization techniques demonstrates enhanced capabilities in hierarchical models.
• Implications suggest a pathway for more efficient analysis, helping various applications in computational mathematics and data science.