LEADER 02347nam 2200337z- 450 001 9910346663403321 005 20231214133523.0 010 $a3-03897-591-5 035 $a(CKB)4920000000095033 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/44618 035 $a(EXLCZ)994920000000095033 100 $a20202102d2019 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDecomposability of Tensors 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 215 $a1 electronic resource (160 p.) 311 $a3-03897-590-7 330 $aTensor decomposition is a relevant topic, both for theoretical and applied mathematics, due to its interdisciplinary nature, which ranges from multilinear algebra and algebraic geometry to numerical analysis, algebraic statistics, quantum physics, signal processing, artificial intelligence, etc. The starting point behind the study of a decomposition relies on the idea that knowledge of elementary components of a tensor is fundamental to implement procedures that are able to understand and efficiently handle the information that a tensor encodes. Recent advances were obtained with a systematic application of geometric methods: secant varieties, symmetries of special decompositions, and an analysis of the geometry of finite sets. Thanks to new applications of theoretic results, criteria for understanding when a given decomposition is minimal or unique have been introduced or significantly improved. New types of decompositions, whose elementary blocks can be chosen in a range of different possible models (e.g., Chow decompositions or mixed decompositions), are now systematically studied and produce deeper insights into this topic. The aim of this Special Issue is to collect papers that illustrate some directions in which recent researches move, as well as to provide a wide overview of several new approaches to the problem of tensor decomposition. 610 $aborder rank and typical rank 610 $aTensor analysis 610 $aRank 610 $aComplexity 700 $aLuca Chiantini (Ed.)$4auth$01315262 906 $aBOOK 912 $a9910346663403321 996 $aDecomposability of Tensors$93032314 997 $aUNINA