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"This book bridges the gap between academics in computer science and traders and explains the basic ideas of the proposed systems and the financial problems in ways that can be understood by readers without previous knowledge on either of the fields. To cement the ideas discussed in the book, software packages are offered that implement the systems described within.The book is structured so that each chapter can be read independently from the others. Chapters 1 and 2 describe evolutionary computation. The third chapter is an introduction to financial engineering problems for readers who are unfamiliar with this area. The following chapters each deal, in turn, with a different problem in the financial engineering field describing each problem in detail and focusing on solutions based on evolutionary computation. Finally, the two appendixes describe software packages that implement the solutions discussed in this book, including installation manuals and parameter explanations."

"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. ​"

"Here is a monograph that describes current research efforts in the application of symbolic computation to several areas, including dynamical systems, differential geometry, Lie algebras, numerical analysis, fluid dynamics, perturbation theory, control theory, and mechanics. The chapters, which illustrate how symbolic computations can be used to study various mathematical structures, are outgrowths of the invited talks that were presented at the NASA-Ames Workshop on The Use of Symbolic Methods to Solve Algebraic and Geometric Problems Arising in Engineering. More than 100 people participated in the two-day conference, which took place in January 1987 at the NASA-Ames Research Center in Moffett Field, California. The field of symbolic computation is becoming increasingly important in science, engineering, and mathematics. The availability of powerful computer algebra systems on workstations has made symbolic computation an important tool for many researchers."

"Focusing on what actuaries need in practice, this introductory account provides readers with essential tools for handling complex problems and explains how simulation models can be created, used and re-used (with modifications) in related situations. The book begins by outlining the basic tools of modelling and simulation, including a discussion of the Monte Carlo method and its use. Part II deals with general insurance and Part III with life insurance and financial risk. Algorithms that can be implemented on any programming platform are spread throughout and a program library written in R is included. Numerous figures and experiments with R-code illustrate the text. The author's non-technical approach is ideal for graduate students, the only prerequisites being introductory courses in calculus and linear algebra, probability and statistics. The book will also be of value to actuaries and other analysts in the industry looking to update their skills."

"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."

"Discrete-time linear systems : theory and design with applications combines system theory and design in order to show the importance of system theory and its role in system design. The book focuses on system theory (including optimal state feedback and optimal state estimation) and system design (with applications to feedback control systems and wireless transceivers, plus system identification and channel estimation)."