Results 91 to 100 of about 1,994 (184)

Generalized low-rank approximation to the symmetric positive semidefinite matrix

AIMS Mathematics
In this paper, we investigate the generalized low rank approximation to the symmetric positive semidefinite matrix in the Frobenius norm: $$\underset{ rank(X)\leq k}{\min} \sum^m_{i=1}\left \Vert A_i - B_i XB_i^T \right \Vert^2_F,$$ where $X$ is an unknown symmetric positive semidefinite matrix and $k$ is a positive integer. We firstly use the property
Chang, Haixia, Li, Chunmei, Huang, Qionghui   +2 more
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A Framework for Coxeter Spectral Classification of Finite Posets and Their Mesh Geometries of Roots

International Journal of Mathematics and Mathematical Sciences, 2013
Following our paper [Linear Algebra Appl. 433(2010), 699–717], we present a framework and computational tools for the Coxeter spectral classification of finite posets J≡(J,⪯).
Daniel Simson, Katarzyna Zając
doaj   +1 more source

Jacobi–Sobolev Orthogonal Polynomials, Differential Properties and Structural Formulas

Axioms
In this paper, we extend some differential and structural results for monic Jacobi–Sobolev orthogonal polynomials, associated with a general discrete Sobolev inner product, with a Jacobi continuous part. We consider finitely many exterior mass points and
Héctor Pijeira-Cabrera, Javier Quintero-Roba, Juan Toribio-Milane   +2 more
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Gaussian mixtures closest to a given measure via optimal transport

Comptes Rendus. Mathématique
Given a determinate (multivariate) probability measure $\mu $, we characterize Gaussian mixtures $\nu _\phi $ which minimize the Wasserstein distance $W_2(\mu ,\nu _\phi )$ to $\mu $ when the mixing probability measure $\phi $ on the parameters $(\mathbf{
Lasserre, Jean B.
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Semidefinite Programming Approaches to Hankel Matrix Approximation and Completion via Primal–Dual Interior-Point Methods

Journal of Mathematics
Data completion techniques offer numerous advantages in various fields. However, completing large datasets that must satisfy specific criteria can be challenging, necessitating the use of approximative completion methods.
Hajar A. Alshaikh, Mohammed M. Alshahrani, Suliman S. Al-Homidan   +2 more
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Coherence Matrix Power Model for Scattering Variation Representation in Multi-Temporal PolSAR Crop Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The multitemporal polarimetric SAR (PolSAR) data contains the scattering change information during the growth of crops. However, the current classification methods usually directly use the addition of features extracted at single-temporal or use the ...
Qiang Yin, Li Gao, Yongsheng Zhou, Yang Li, Fan Zhang, Carlos Lopez-Martinez, Wen Hong   +6 more
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Lyapunov matrix equations with positive semidefinite input matrices

Electronics Letters, 1968
An algebraic criterion is derived, which establishes whether or not the negative-semidefinite first difference of a quadratic-form Lyapunov function for a linear constant-coefficient difference equation, or the derivative of such a function for a linear constant-coefficient differential equation, can vanish identically along any trajectory of the free ...
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Singular value inequalities of matrices via increasing functions

Journal of Inequalities and Applications
Let A, B, X, and Y be n × n $n\times n$ complex matrices such that A is self-adjoint, B ≥ 0 $B\geq 0$ , ± A ≤ B $\pm A\leq B$ , max ( ∥ X ∥ 2 , ∥ Y ∥ 2 ) ≤ 1 $\max ( \Vert X \Vert ^{2}, \Vert Y \Vert ^{2} ) \leq 1$ , and let f be a nonnegative increasing
Wasim Audeh, Anwar Al-Boustanji, Manal Al-Labadi, Raja’a Al-Naimi   +3 more
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A connection between positive semidefinite and euclidean distance matrix completion problems

Linear Algebra and its Applications, 1998
The results obtained for positive semidefinite (PSD) and Euclidean distance (ED) matrix completion problems are very similar. Even though there is a strong relationship between the PSD matrices and ED matrices, it was not clear how to link the two completion problems.
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Comparison theorems for the minimum eigenvalue of a random positive-semidefinite matrix

CoRR
This paper establishes a new comparison principle for the minimum eigenvalue of a sum of independent random positive-semidefinite matrices. The principle states that the minimum eigenvalue of the matrix sum is controlled by the minimum eigenvalue of a Gaussian random matrix that inherits its statistics from the summands.
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