Results 11 to 20 of about 3,041 (214)
Robust Differentiable SVD [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Eigendecomposition of symmetric matrices is at the heart of many computer vision algorithms. However, the derivatives of the eigenvectors tend to be numerically unstable, whether using the SVD to compute them analytically or using the Power Iteration (PI) method to approximate them.
Wei Wang 0108 +4 more
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On GROUSE and incremental SVD [PDF]
2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013 GROUSE (Grassmannian Rank-One Update Subspace Estimation) is an incremental algorithm for identifying a subspace of Rn from a sequence of vectors in this subspace, where only a subset of components of each vector is revealed at each iteration. Recent analysis has shown that GROUSE converges locally at an expected linear rate, under certain assumptions.
Laura Balzano, Stephen J. Wright 0001
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CoRR, 2021 The code can be accessed through https://github.com/Vicky-Zh/E ...
Huiwen Wang, Yanwen Zhang, Jichang Zhao
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Journal of High Energy Physics, 2023 Abstract In this paper, we introduce a new quantity called SVD entanglement entropy. This is a generalization of entanglement entropy in that it depends on two different states, as in pre- and post-selection processes. This SVD entanglement entropy takes non-negative real values and is bounded by the logarithm of the Hilbert space ...
Arthur J. Parzygnat +3 more
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On the Computation of the SVD of Fourier Submatrices
Journal of Scientific Computing, 2023 Contiguous submatrices of the Fourier matrix are known to be ill-conditioned. In a recent paper in SIAM Review A. Barnett has provided new bounds on the rate of ill-conditioning of the discrete Fourier submatrices. In this paper we focus on the corresponding singular value decomposition.
Huybrechs, Daan +2 more
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, 2006 In this paper, we propose a path-following method for computing a curve of equilibria of a dynamical system, based upon the smooth Singular Value Decomposition (SVD) of the Jacobian matrix. Our method is capable of detecting fold points, and continuing past folds.
L. DIECI +2 more
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The American Mathematical Monthly, 2012 The singular value decomposition (SVD) is a popular matrix factorization that has been used widely in applications ever since an efficient algorithm for its computation was developed in the 1970s. In recent years, the SVD has become even more prominent due to a surge in applications and increased computational memory and speed.
Martin, C, Porter, M
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CORDIC arithmetic for an SVD processor [PDF]
1987 IEEE 8th Symposium on Computer Arithmetic (ARITH), 1987 Abstract Arithmetic issues in the calculation of the Singular Value Decomposition (SVD) are discussed. Traditional algorithms using hardware division and square root are replaced with the special-purpose CORDIC algorithms for computing vector rotations and inverse tangents.
Joseph R. Cavallaro, Franklin T. Luk
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Obrazy eschatologiczne w ludowych pieśniach pogrzebowych w Opoczyńskiem
Studia Etnologiczne i Antropologiczne, 2018 The article was based on the ethnographic field research on funeral rites in the Opoczno Region conducted by the author, and the literature of the subject. The elaboration presents visions of heaven, purgatory and hell described in funeral songs.
Zdzisław Kupisiński SVD
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The covariability of sea surface temperature and MAM rainfall on East Africa using singular value decomposition analysis [PDF]
Geographica Pannonica, 2020 The study assesses the covariability of Sea Surface Temperature (SST) and March to May (MAM) rainfall variability on East Africa (EA) from 1981 to 2018. Singular Value Decomposition (SVD) analysis reveals the significant influence of SST anomalies on MAM
Kisesa Makula Exavery +5 more
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