Results 31 to 40 of about 1,148,156 (325)
Kaczmarz method with oblique projection
Results in Applied Mathematics, 2022 The popular randomized Kaczmarz method is a single random orthogonal projection method. In this paper, a single randomized Kaczmarz method with oblique projection is discussed.
Weiguo Li +3 more
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Convergence rates of spectral orthogonal projection approximation for functions of algebraic and logarithmatic regularities [PDF]
SIAM Journal on Numerical Analysis, 2020 Based on the Hilb type formula between Jacobi polynomials and Bessel functions, optimal decay rates on Jacobi expansion coefficients are derived, by applying van der Corput type lemmas, for functions of logarithmatic singularities, which leads to the ...
S. Xiang
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Performance Analysis of Oblique Projection Filtering Based on Polarization Sensitive Array
Leida xuebao, 2013 The output SINR (Signal to Interference and Noise Ratio) of oblique projection and orthogonal projection filtering based on polarization sensitive array is investigated.
Tian Jing, Liao Gui-sheng, Yang Zhi-wei
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Orthogonal random projection for tensor completion
IET Computer Vision, 2020 The low‐rank tensor completion problem, which aims to recover the missing data from partially observable data. However, most of the existing tensor completion algorithms based on Tucker decomposition cannot avoid using singular value decomposition (SVD ...
Yali Feng, Guoxu Zhou
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The identities for generalized Fibonacci numbers via orthogonal projection [PDF]
, 2019 In this paper, we consider the space R(p, 1) of generalized Fibonacci sequences and orthogonal bases of this space. Using these orthogonal bases, we obtain the orthogonal projection onto a subspace R(p, 1) of R-n.
Kocer, E. Gokcen, Alp, Yasemin
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Uncertainty Analysis of Neutron Diffusion Eigenvalue Problem Based on Reduced-order Model
Yuanzineng kexue jishu, 2023 In order to improve the efficiency of core physical uncertainty analysis based on sampling statistics, the proper orthogonal decomposition (POD) and Galerkin projection method were combined to study the application feasibility of reduced-order model ...
In order to improve the efficiency of core physical uncertainty analysis based on sampling statistics, the proper orthogonal decomposition (POD) and Galerkin projection method were combined to study the application feasibility of reduced-order model based on POD-Galerkin method in core physical uncertainty analysis. The two-dimensional two group TWIGL benchmark question was taken as the research object, the key variation characteristics of the core flux distribution were extracted under the finite perturbation of the group constants of each material region, and the full-order neutron diffusion problem was projected on the variation characteristics to establish a reduced-order neutron diffusion model. The reduced-order model was used to replace the full-order model to carry out the uncertainty analysis of the group constants of the material region. The results show that the bias of the mathematical expectation of keff calculated by reduced-order and full-order models is close to 1 pcm. In addition, compared with the calculation time required for uncertainty analysis of full-order model, the analysis time of reduced-order model (including the calculation time of the full-order model required for the construction of reduced-order model) is only 11.48%, which greatly improves the efficiency of uncertainty analysis. The biases of mathematical expectation of keff calculated by reduced-order and full-order models based on Latin hypercube sampling and simple random sampling are less than 8 pcm, and under the same sample size, the bias from the Latin hypercube sampling result is smaller. From the TWIGL benchmark test results, under the same sample size, Latin hypercube sampling method is more recommended for POD-Galerkin reduced-order model.
doaj
Some results about g-frames in Hilbert spaces [PDF]
MATEC Web of Conferences, 2022 The concept of g-frame is a natural extension of the frame. This article mainly discusses the relationship between some special bounded linear operators and g-frames, and characterizes the properties of g-frames.
Luo Lan, Leng Jingsong, Xie Tingting
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Uniform analyticity of orthogonal projections [PDF]
Transactions of the American Mathematical Society, 1989 Let X X denote the circle T
Coifman, R. R., Murray, M. A. M.
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Orthogonal projections of hypercubes
Physical Review EProjections of hypercubes have been applied to visualize high-dimensional binary state spaces in various scientific fields. Conventional methods for projecting hypercubes, however, face practical difficulties. Manual methods require nontrivial adjustments of the projection basis, while optimization-based algorithms limit the interpretability and ...
Yoshiaki Horiike, Shin Fujishiro
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The Accurate Method for Computing the Minimum Distance between a Point and an Elliptical Torus
Computers, 2016 We present an accurate method to compute the minimum distance between a point and an elliptical torus, which is called the orthogonal projection problem.
Xiaowu Li +4 more
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