Results 71 to 80 of about 2,237,659 (305)
Randomized Matrix Decompositions Using R
Journal of Statistical Software, 2019 Matrix decompositions are fundamental tools in the area of applied mathematics, statistical computing, and machine learning. In particular, low-rank matrix decompositions are vital, and widely used for data analysis, dimensionality reduction, and data ...
N. Benjamin Erichson +3 more
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Advanced Engineering Materials, EarlyView.Residual adhesive after electrode loading in adhesive‐assisted resistance spot welding is quantified through a traceable experimental‐to‐digital workflow. Chromatic confocal topography provides calibrated surface‐height data, while OpenCV detects the electrode imprint and integrates adhesive height into comparable volume metrics.
Sung‐Min Wi, Jiangdong Zhao
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Fast matrix decomposition in F2
CoRR, 2012 20 pages, 4 figure, 6 tables, 1 ...
Bertolazzi, Enrico, Rimoldi, Anna
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Fast Polar Decomposition of an Arbitrary Matrix [PDF]
SIAM Journal on Scientific and Statistical Computing, 1990 The polar decomposition of an $m \times n$ matrix A of full rank, where $m \geqq n$, can be computed using a quadratically convergent algorithm of Higham [SIAM J. Sci. Statist. Comput., 7(1986), pp. 1160–1174]. The algorithm is based on a Newton iteration involving a matrix inverse.
Nicholas J. Higham, Robert S. Schreiber
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Adaptive Foam 3D Printing of Ultralight and Multifunctional Materials
Advanced Engineering Materials, EarlyView.Adaptive foam 3D printing, enabled by expandable microspheres, imparts cellular structures to thermoplastic and thermosetting polymers, manufactured through a variety of processes including fused filament fabrication, direct ink writing, digital light processing, and inkjet printing.
Nariman Rajabifar, Amir Ameli
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Relative-Error $CUR$ Matrix Decompositions [PDF]
SIAM Journal on Matrix Analysis and Applications, 2008 Many data analysis applications deal with large matrices and involve approximating the matrix using a small number of ``components.'' Typically, these components are linear combinations of the rows and columns of the matrix, and are thus difficult to interpret in terms of the original features of the input data.
Petros Drineas +2 more
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SVD Algorithm in the Application of the MIMO System Model Order Reduction
Journal of Harbin University of Science and Technology, 2017 For linear time invariant high-order system calculation and analysis model is difficult to directly，so we need to study on reduction of the model According to the basic theory of model order reduction Lyapunov equation model order solution to linear time
LU Fang-ming, ZHOU Lin
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Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy
Advanced Engineering Materials, EarlyView.Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang +6 more
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Investigating the Low‐Temperature Phase Stability of the Binary Ta–W System
Advanced Engineering Materials, EarlyView.Atomistic simulations show that the binary Ta–W system forms ordered intermetallic phases, B2‐TaW and D03‐TaW3, as 0 K ground states. Configurational entropy, however, lowers the free energy of the disordered bcc solid solution, which becomes the stable phase above about 400 K.
Klemens Lechner +7 more
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Sparse and Low-Rank Matrix Decompositions [PDF]
IFAC Proceedings Volumes, 2009 Abstract Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-rank components. Such a problem arises in a number of applications in model and system identification, but obtaining an exact solution is NP-hard in general.
Venkat Chandrasekaran +3 more
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