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"Special numerical techniques are already needed to deal with nxn matrices for large n.Tensor data are of size nxnx...xn=n^d, where n^d exceeds the computer memory by far. They appear for problems of high spatial dimensions. Since standard methods fail, a particular tensor calculus is needed to treat such problems. The monograph describes the methods how tensors can be practically treated and how numerical operations can be performed. Applications are problems from quantum chemistry, approximation of multivariate functions, solution of pde, e.g., with stochastic coefficients, etc. ​"

"Tensor yang dipandang sebagai multidimensional array adalah bentuk umum dari suatu matriks. Oleh karena itu, dapat dikonstruksi bentuk umum dari hasil kali matriks yang disebut sebagai hasil kali tensor. Tujuan dari tulisan ini adalah menjelaskan inversi kiri dan inversi kanan suatu tensor. Pada tulisan ini disajikan karakteristik eksistensi inversi kiri dan inversi kanan orde k dari suatu tensor. Disajikan pula hasil terkait keserupaan suatu tensor.

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Tensor, which is seemed as multidimensional array, is a general form of matrix. Therefore, tensor could be constructed into general form of matrix product which is called tensor product. The aim of this writing was to explain the right and left inversion of tensor. In this research, there were characteristics of right and left extension of orde k of tensor provided, in addition, there was also a result involved of the tensor similarity."

"Dalam konteks matematika komputasi, tensor sering dipandang sebagai larik multidimensi, dengan jumlah dimensinya disebut sebagai orde tensor tersebut. Tensor dapat digunakan untuk merepresentasikan berbagai jenis data, seperti data gambar dan data psikometri. Salah satu masalah yang penting dalam komputasi tensor adalah aproksimasi rank rendah tensor. Untuk sebuah tensor A, masalah aproksimasi rank rendah adalah mencari tensor B yang nilainya paling mendekati tensor A tetapi memiliki rank tertentu yang lebih kecil dari rank A. Untuk tensor orde 2 (matriks), Teorema Eckart-Young-Mirsky menjelaskan bahwa masalah aproksimasi rank rendah matriks dapat diselesaikan dengan dekomposisi nilai singular (SVD). Akan tetapi, memperumum Teorema Eckart-Young-Mirsky untuk tensor adalah sebuah persoalan yang rumit. Masalah utamanya adalah, dalam kasus tensor, ada beberapa definisi rank yang berbeda. Masing-masing definisi rank dihasilkan dengan memperumum sifat-sifat tertentu dari fungsi rank matriks dan dapat menghasilkan nilai yang berbeda-beda untuk tensor yang sama; permasalahan tersebut adalah pokok bahasan skripsi ini. Skripsi ini dimulai dengan membahas konsep-konsep dasar dalam komputasi tensor. Lalu, akan dibahas mengenai tiga definisi konsep rank tensor. Untuk masing-masing definisi rank tensor, akan dipaparkan dekomposisi tensor yang berkaitan; dekomposisi-dekomposisi tensor ditujukan untuk memperumum SVD. Lalu, konsep rank dan dekomposisi tensor digabungkan dalam pembahasan masalah aproksimasi rank tensor. Pembahasan dilanjutkan dengan pembahasan hasil kali *M. Hasil kali *M dibuat untuk membentuk sebuah kerangka umum sebagai upaya menggabungkan beberapa dekomposisi tensor yang telah dibahas sebelumnya. Terakhir, dijelaskan mengenai berbagai sifat dan keunggulan teoretis kerangka hasil kali *M.

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In the context of computational mathematics, tensors are often viewed as multidimensional arrays, with the number of dimensions referred to as the order of the tensor. Tensors can be used to represent various types of data, such as image data and psychometric data. One important problem in tensor computation is the low-rank approximation of tensors. For a tensor A, the low-rank approximation problem is to find the tensor B whose entries are closest to the tensor A but has a certain rank that is smaller than the rank of A. For tensors of order two (matrices), the Eckart-Young-Mirsky theorem says that the matrix low-rank approximation problem can be solved by truncating its singular value decomposition (SVD). However, generalizing the Eckart-Young-Mirsky theorem to tensors is a complicated problem. The main problem is that there are several different definitions of rank in the case of tensors. Each definition of rank is generated by generalizing certain properties of the matrix rank and can yield different values for the same tensor; that problem is the subject of this thesis. This thesis begins by discussing the basic concepts of tensor computation. Then, three definitions of the concept of rank tensor will be addressed. For each definition of rank tensor, the corresponding tensor decomposition is presented; the tensor decompositions are intended to generalize the SVD. Then, the concepts of rank and tensor decomposition are combined to discuss the rank tensor approximation problem. The discussion continues with the discussion of the product of *M. The product of *M is made to form a general framework as an attempt to combine several tensor decompositions that have been discussed previously. Finally, various properties and theoretical advantages of the *M product framework are explained."

"The aim of this book is to facilitate the use of Stokes' theorem in applications. The text takes a differential geometric point of view and provides for the student a bridge between pure and applied mathematics by carefully building a formal rigorous development of the topic and following this through to concrete applications in two and three variables. Several practical methods and many solved exercises are provided. This book tries to show that vector analysis and vector calculus are not always at odds with one another. Key topics include, vectors and vector fields, line integrals, regular k-surfaces, flux of a vector field, orientation of a surface, differential forms, Stokes' theorem, and divergence theorem. "

"This work is a concise introduction to spectral theory of Hilbert space operators. Its emphasis is on recent aspects of theory and detailed proofs, with the primary goal of offering a modern introductory textbook for a first graduate course in the subject. The coverage of topics is thorough, as the book explores various delicate points and hidden features often left untreated."

"This is the fifth and revised edition of a well-received textbook that aims at bridging the gap between the engineering course of tensor algebra on the one hand and the mathematical course of classical linear algebra on the other hand. In accordance with the contemporary way of scientific publication, a modern absolute tensor notation is preferred throughout. The book provides a comprehensible exposition of the fundamental mathematical concepts of tensor calculus and enriches the presented material with many illustrative examples. As such, this new edition also discusses such modern topics of solid mechanics as electro- and magnetoelasticity. In addition, the book also includes advanced chapters dealing with recent developments in the theory of isotropic and anisotropic tensor functions and their applications to continuum mechanics. Hence, this textbook addresses graduate students as well as scientists working in this field and in particular dealing with multi-physical problems. In each chapter numerous exercises are included, allowing for self-study and intense practice. Solutions to the exercises are also provided."