Higher-order tensor methods are intensively studied in many disciplines nowadays. The developments gradually allow us to move from classical vector and matrix based methods in applied mathematics and mathematical engineering to methods that involve tensors of arbitrary order. This step from linear transformations, quadratic and bilinear forms to polynomials and multilinear forms is relevant for the most diverse applications. Furthermore, tensor methods have firm roots in multilinear algebra, algebraic geometry, numerical mathematics and optimization.
This workshop will bring together researchers investigating tensor decompositions and their applications. It will feature a series of invited talks by leading experts and contributed presentations on specific problems. It will be preceded by a two-day winter school on tensor methods.
TDA 2016 (January 18th - 22th) is held in cooperation with the Society for Industrial and Applied Mathematics (SIAM) and has been endorsed by the International Linear Algebra Society (ILAS). Part of the workshop is supported by the ERC Advanced Grant BioTensors.
Earlier editions were held in Luminy, Marseille, France (TDA 2005) and Monopoli, Bari, Italy (TDA 2010).
Topics of interest include:
- Algebraic properties of tensor decompositions
- Numerical computation of tensor decompositions
- Tensor-based optimization
- Tensor-based scientific computing
- Algebraic geometry
- Computational complexity
- Polynomial optimization
- Systems of polynomial equations
- Tensor-based signal processing
- Tensor-based machine learning
- Tensor-based data mining
- Tensor-based analysis of graphs, networks and hyperlink data
- Tensor-based system identification
- Independent component analysis, latent variable analysis and factor analysis
- Data fusion and multimodal data analysis
- Applications in chemometrics, psychometrics, econometrics and social sciences
- Applications in telecommunication
- Applications in array processing
- Applications in bioinformatics and biomedical engineering
- Applications in quantum information theory and quantum computing
- Applications in big data analysis