In the field of data science and engineering, tensors have become an essential tool for representing and analyzing large-scale multidimensional data. As data dimensions increase, so does the complexity of these structures, along with their correlations and redundancies, which pose significant challenges for data collection and processing. Tensors, as high-order matrices, offer a more representative framework for capturing the intrinsic structure of such data. Consequently, tensor low-rank modeling has emerged as a vital technique, finding applications in diverse fields such as signal processing, computer vision, data mining, and bioinformatics. Despite its potential, this domain still faces challenges in terms of optimizing tensor representation and processing methodologies.

This Research Topic aims to advance methods in optimization and numerical linear algebra through the development and solution of tensor low-rank models. By promoting the integration of these methods, the Research Topic seeks to achieve more efficient and scalable solutions applicable to real-world problems. The exploration will cover fundamental steps like tensor decomposition and equation solving, thereby contributing to the optimization frameworks necessary for handling high-dimensional data proficiently. Critical objectives include refining existing techniques and innovating new approaches, ultimately enhancing performance in practical applications across varied disciplines.

To gather further insights into optimizing high-dimensional tensor data analysis, we welcome articles addressing, but not limited to, the following themes:

- Tensor and matrix decomposition techniques, including deterministic and randomized approaches.

- Development of efficient algorithms for solving tensor and matrix equations.

- Innovative optimization algorithms for low-rank tensor models, such as tensor completion and recovery.

- Practical applications and implementations of tensor low-rank models in various scientific and engineering domains.

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Advancements in tensor low-rank modeling for data-intensive applications