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Książki na temat „Decomposition de tenseur”

Autor: Grafiati

Data publikacji: 14 grudnia 2024

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1

Cheng, Lei, Zhongtao Chen i Yik-Chung Wu. Bayesian Tensor Decomposition for Signal Processing and Machine Learning. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22438-6.

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2

Theocaris, Pericles S. On a general theory of anisotropy of matter: The spectral decomposition of the compliance tensor : application to crystallography. Athēnai: Grapheion Dēmosieumatōn tēs Akadēmias Athēnōn, 1999.

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3

Kondrat'ev, Gennadiy. Clifford Geometric Algebra. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1832489.

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The monograph is devoted to the fundamental aspects of geometric algebra and closely related issues. The category of Clifford algebras is considered as the conjugate category of vector spaces with a quadratic form. Possible constructions in this category and internal algebraic operations of an algebra with a geometric interpretation are studied. An application to the differential geometry of a Euclidean manifold based on a shape tensor is included. We consider products, coproducts and tensor products in the category of associative algebras with application to the decomposition of Clifford algebras into simple components. Spinors are introduced. Methods of matrix representation of the Clifford algebra are studied. It may be of interest to students, postgraduates and specialists in the field of mathematics, physics and cybernetics.

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4

Ninul, Anatolij Sergeevič. Tensor Trigonometry. Moscow, Russia: Fizmatlit Publisher, 2021.

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5

Ninul, Anatolij Sergeevič. Tenzornaja trigonometrija: Teorija i prilozenija / Theory and Applications /. Moscow, Russia: Mir Publisher, 2004.

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6

Structured Tensor Recovery and Decomposition. [New York, N.Y.?]: [publisher not identified], 2017.

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7

Maggiore, Michele. Helicity decomposition of metric perturbations. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198570899.003.0009.

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Decomposition of the perturbations over FRW into scalar, vector and tensor perturbations. Physical and unphysical degrees of freedom. Gauge-invariant metric perturbations, Bardeen variables. Gauge-invariant perturbations of the energy-momentum tensor

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8

Favier, Gérard. Matrix and Tensor Decompositions in Signal Processing. Wiley & Sons, Incorporated, John, 2021.

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9

Favier, Gérard. Matrix and Tensor Decompositions in Signal Processing. Wiley & Sons, Incorporated, John, 2021.

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10

Favier, Gérard. Matrix and Tensor Decompositions in Signal Processing. Wiley & Sons, Incorporated, John, 2021.

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11

Favier, Gérard. Matrix and Tensor Decompositions in Signal Processing. Wiley & Sons, Incorporated, John, 2021.

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12

Adali, Tulay, Dana Lahat i Christian Jutten. Matrix and Tensor Decomposition: Application to Data Fusion and Analysis. Elsevier Science & Technology Books, 2022.

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13

Lee, Namgil, Anh-Huy Phan, Danilo P. Mandic, Andrzej Cichocki i Ivan Oseledets. Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 1 Low-Rank Tensor Decompositions. Now Publishers, 2016.

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14

Bayesian Tensor Decomposition for Signal Processing and Machine Learning: Modeling, Tuning-Free Algorithms, and Applications. Springer International Publishing AG, 2024.

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15

Cheng, Lei, Zhongtao Chen i Yik-Chung Wu. Bayesian Tensor Decomposition for Signal Processing and Machine Learning: Modeling, Tuning-Free Algorithms and Applications. Springer International Publishing AG, 2023.

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16

Kaye, Phillip, Raymond Laflamme i Michele Mosca. An Introduction to Quantum Computing. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780198570004.001.0001.

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This concise, accessible text provides a thorough introduction to quantum computing - an exciting emergent field at the interface of the computer, engineering, mathematical and physical sciences. Aimed at advanced undergraduate and beginning graduate students in these disciplines, the text is technically detailed and is clearly illustrated throughout with diagrams and exercises. Some prior knowledge of linear algebra is assumed, including vector spaces and inner products. However, prior familiarity with topics such as tensor products and spectral decomposition is not required, as the necessary material is reviewed in the text.

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17

Oertel, Gerhard. Stress and Deformation. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195095036.001.0001.

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Students of geology who may have only a modest background in mathematics need to become familiar with the theories of stress, strain, and other tensor quantities, so that they can follow, and apply to their own research, developments in modern, quantitative geology. This book, based on a course taught by the author at UCLA, can provide the proper introduction. Included throughout the eight chapters are 136 complex problems, advancing from vector algebra in standard and subscript notations, to the mathematical description of finite strain and its compounding and decomposition. Fully worked solutions to the problems make up the largest part of the book. With their help, students can monitor their progress, and geologists will be able to utilize subscript and matrix notations and formulate and solve tensor problems on their own. The book can be successfully used by anyone with some training in calculus and the rudiments of differential equations.

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18

On Natural Coalgebra Decompositions of Tensor Algebras and Loop Suspensions (Memoirs of the American Mathematical Society). American Mathematical Society, 2000.

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19

Maggiore, Michele. Gravitational Waves. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198570899.001.0001.

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A comprehensive and detailed account of the physics of gravitational waves and their role in astrophysics and cosmology. The part on astrophysical sources of gravitational waves includes chapters on GWs from supernovae, neutron stars (neutron star normal modes, CFS instability, r-modes), black-hole perturbation theory (Regge-Wheeler and Zerilli equations, Teukoslky equation for rotating BHs, quasi-normal modes) coalescing compact binaries (effective one-body formalism, numerical relativity), discovery of gravitational waves at the advanced LIGO interferometers (discoveries of GW150914, GW151226, tests of general relativity, astrophysical implications), supermassive black holes (supermassive black-hole binaries, EMRI, relevance for LISA and pulsar timing arrays). The part on gravitational waves and cosmology include discussions of FRW cosmology, cosmological perturbation theory (helicity decomposition, scalar and tensor perturbations, Bardeen variables, power spectra, transfer functions for scalar and tensor modes), the effects of GWs on the Cosmic Microwave Background (ISW effect, CMB polarization, E and B modes), inflation (amplification of vacuum fluctuations, quantum fields in curved space, generation of scalar and tensor perturbations, Mukhanov-Sasaki equation,reheating, preheating), stochastic backgrounds of cosmological origin (phase transitions, cosmic strings, alternatives to inflation, bounds on primordial GWs) and search of stochastic backgrounds with Pulsar Timing Arrays (PTA).

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20

De Clercq, Karen. Syncretisms and the Morphosyntax of Negation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190876746.003.0007.

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This chapter illustrates how syncretisms can be used as a tool to diagnose hidden structure inside what is usually considered an indivisible unit, i.e. a negative marker. Based on semantic, scopal, stacking, and functional properties of negative markers, it is proposed that negative markers can be classified in four groups: scalar quantity markers, classifier markers, focus markers, and tense markers. A study of these four different types of markers in a typological sample shows that meaningful syncretism patterns can be detected. If the markers are ordered in such a way that syncretic markers are contiguous and that no ABA patterns arise, then the derived sequence reflects the natural semantic scope of negation, i.e. from wide to narrow scope or from narrow to wide. This result leads to a decomposition of a negative marker into five syntactico-semantic features, i.e. Neg, Q, Class, Foc, and T.

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