Results 11 to 20 of about 914,727 (265)
On the singular value decomposition of (skew-)involutory and (skew-)coninvolutory matrices
Special Matrices, 2020 The singular values σ > 1 of an n × n involutory matrix A appear in pairs (σ, 1σ{1 \over \sigma }). Their left and right singular vectors are closely connected. The case of singular values σ = 1 is discussed in detail. These singular values may appear in
Faßbender Heike, Halwaß Martin
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Fast singular value thresholding without singular value decomposition [PDF]
Methods and Applications of Analysis, 2013 We are interested in solving the following minimization problem Dτ (Y ) := arg min X∈Rm×n 1 2 ∥Y −X∥F + τ∥X∥∗, where Y ∈ Rm×n is a given matrix, and ∥ ⋅ ∥F is the Frobenius norm and ∥ ⋅ ∥∗ the nuclear norm. This problem serves as a basic subroutine in many popular numerical schemes for nuclear norm minimization problems, which arise from low rank ...
Cai, Jianfeng, Stanley, Osher
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On the N-spectrum of oriented graphs
Open Mathematics, 2020 Given any digraph D, its non-negative spectrum (or N-spectrum, shortly) consists of the eigenvalues of the matrix AA T, where A is the adjacency matrix of D.
Abudayah Mohammad +2 more
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Robust Parameter Estimation of an Empirical Manoeuvring Model Using Free-Running Model Tests
Journal of Marine Science and Engineering, 2021 The work presents the identification and validation of the hydrodynamic coefficients for the surge, sway, and yaw motion. This is performed in two ways: using simulated data and free-running test data.
Ana Catarina Costa +2 more
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Theoretical and Natural ScienceSingular Value Decomposition (SVD) is a very important matrix factorization technique in linear algebra which generalizes the eigenvalue decomposition to both non square and non symmetric matrices. This report explains the theoretical foundation of SVD by numerical examples and the comparison of SVD with eigenvalue decomposition on the basis of ...
J. Douglas Walker, Noah M. McLean
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Variational Quantum Singular Value Decomposition [PDF]
Quantum, 2021 Singular value decomposition is central to many problems in engineering and scientific fields. Several quantum algorithms have been proposed to determine the singular values and their associated singular vectors of a given matrix.
Xin Wang, Zhixin Song, Youle Wang
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The Singular Values of the GOE [PDF]
, 2015 As a unifying framework for examining several properties that nominally involve eigenvalues, we present a particular structure of the singular values of the Gaussian orthogonal ensemble (GOE): the even-location singular values are distributed as the ...
Bornemann, Folkmar, La Croix, Michael
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The Stability Analysis of Linear Systems with Cauchy—Polynomial-Vandermonde Matrices
Axioms, 2023 The numerical approximation of both eigenvalues and singular values corresponding to a class of totally positive Bernstein–Vandermonde matrices, Bernstein–Bezoutian structured matrices, Cauchy—polynomial-Vandermonde structured matrices, and quasi ...
Mutti-Ur Rehman +3 more
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Singularity, Wielandt’s lemma and singular values
Journal of Computational and Applied Mathematics, 2010 The authors obtain some upper and lower bounds for the largest and the smallest singular values of certain complex matrices based on the entries and diagonal dominance. A relationship between largest singular value of a block matrix and its block norm matrix is obtained. Numerical examples are given to demonstrate the usefulness of their results.
Li, Hou-Biao +3 more
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The Smallest Singular Values and Vector-Valued Jack Polynomials [PDF]
, 2018 There is a space of vector-valued nonsymmetric Jack polynomials associated with any irreducible representation of a symmetric group. Singular polynomials for the smallest singular values are constructed in terms of the Jack polynomials.
Dunkl, Charles F.
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