LEADER 02672nam 22004453a 450 001 9910346663403321 005 20250203235433.0 010 $a9783038975915 010 $a3038975915 024 8 $a10.3390/books978-3-03897-591-5 035 $a(CKB)4920000000095033 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/44618 035 $a(ScCtBLL)56c1bf26-1b4e-4d57-af68-87cab0cd3952 035 $a(OCoLC)1163850292 035 $a(oapen)doab44618 035 $a(EXLCZ)994920000000095033 100 $a20250203i20192019 uu 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aDecomposability of Tensors$fLuca Chiantini 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 210 1$aBasel, Switzerland :$cMDPI,$d2019. 215 $a1 electronic resource (160 p.) 311 08$a9783038975908 311 08$a3038975907 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 $aChiantini$b Luca$030380 801 0$bScCtBLL 801 1$bScCtBLL 906 $aBOOK 912 $a9910346663403321 996 $aDecomposability of Tensors$94322967 997 $aUNINA