Titlebook: Extraction of Quantifiable Information from Complex Systems; Stephan Dahlke,Wolfgang Dahmen,Harry Yserentant Book 2014 Springer Internatio

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Allan MacLean,Pernille Marqvardsens a robust framework for approximate arithmetic operations with tensors based on rank truncations, which can be exploited in iterative algorithms. On the other hand, it can be used for the direct approximation of high-dimensional data stemming, e.g., from the discretisation of multivariate functions

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ESG Factors in the Investment Processwn Tucker format into a hierarchical framework, by combining its favourable characteristics with low-order scaling properties. We demonstrate the basic concept of subspace approximation and higher order SVD (HOSVD), and how to extend this in a hierarchical way. We highlight that the present tensor r

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ome of these results and propose a family of algorithms to compute nonlinear eigenvectors which encompasses previous work as special cases. We provide a detailed analysis of the properties and the convergence behavior of these algorithms and then discuss their application in the area of balanced gra

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https://doi.org/10.1007/978-94-011-9845-5assets and require the explicit modeling of the dependence structure of these assets. We follow the approach of Kraft and Steffensen (Rev Finance 11:209–252, 2006), who explicitly describe the possible value combinations of the assets via a Markov chain with a portfolio state space. As the number of

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https://doi.org/10.1007/978-3-8350-5492-9ool. Efficient algorithms like the fast Fourier transform (FFT) have to be customised to these thinner discretisations and we focus on two major topics regarding the Fourier analysis of high-dimensional functions: We present stable and effective algorithms for the fast evaluation and reconstruction

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