Results 81 to 90 of about 2,874 (210)
Sparsifying Sums of Positive Semidefinite Matrices
In this paper, we revisit spectral sparsification for sums of arbitrary positive semidefinite (PSD) matrices. Concretely, for any collection of PSD matrices $\mathcal{A} = \{A_1, A_2, \ldots, A_r\} \subset \mathbb{R}^{n \times n}$, given any subset $T \subseteq [r]$, our goal is to find sparse weights $μ\in \mathbb{R}_{\geq 0}^r$ such that $(1 - ε ...
Arpon Basu +3 more
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Sparse Minimum Redundancy Maximum Relevance for Feature Selection
Scandinavian Journal of Statistics, EarlyView.ABSTRACT We propose a feature screening method that integrates both feature–feature and feature–target relationships. Inactive features are identified via a penalized minimum Redundancy Maximum Relevance (mRMR) procedure, which is the continuous version of the classical mRMR penalized by a non‐convex regularizer, and where the parameters estimated as ...
Peter Naylor +3 more
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A permanent inequality for positive semidefinite matrices
The Electronic Journal of Linear Algebra, 2023 In this paper, we prove an inequality involving the permanent of a positive semidefinite matrix and its leading submatrices. We obtain a result in the similar spirit of Bapat-Sunder per-max conjecture.
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Inertia of partial transpose of positive semidefinite matrices [PDF]
, 2023 We show that the partial transpose of $9\times 9$ positive semidefinite matrices do not have inertia (4,1,4) and (3,2,4). It solves an open problem in "LINEAR AND MULTILINEAR ALGEBRA, Changchun Feng et al, 2022".
Chen, Lin +3 more
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Frequency‐dependent contraction rates for the Bayesian method to the inverse source problem
Transactions of the London Mathematical Society, Volume 13, Issue 1, December 2026.Abstract This paper addresses an inverse source problem for acoustic waves in a range of frequencies. Our study has two main goals. First, although the problem is severely ill‐posed with a logarithmic stability estimate, we demonstrate, through careful analysis of the forward map's singular values, that increasing the frequency range enhances stability,
Pu‐Zhao Kow, Jenn‐Nan Wang
wiley +1 more source
Certain Positive Semidefinite Matrices of Special Functions [PDF]
, 2018 Special functions are often defined as a Fourier or Laplace transform of a positive measure, and the positivity of the measure manifests as positive definiteness of certain matrices. The purpose of this expository note is to give a sample of such positive definite matrices to demonstrate this connection for some well-known special functions such as ...
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An Inequality for Positive Semidefinite Hermitian Matrices(1) [PDF]
, 1974 Let A and B be positive semidefinite Hermitian n-square matrices. If A—B is positive semidefinite, write A≥B. Haynsworth [1] has proved that if A≥B then det(A+B)≥det A+n det B.Let G be a subgroup of the symmetric group, Sn, and let λ be a character on G.
Russell Merris
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A Geometric Mean of Parameterized Arithmetic and Harmonic Means of Convex Functions
Abstract and Applied Analysis, 2012 The notion of the geometric mean of two positive reals is extended by Ando (1978) to the case of positive semidefinite matrices A and B. Moreover, an interesting generalization of the geometric mean A # B of A and B to convex functions was introduced by ...
Sangho Kum, Yongdo Lim
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Bayesian Implementation of the Factor‐Analytic Mixed Model and Application to Embeddings
Stat, Volume 15, Issue 3, September 2026.ABSTRACT Mixed models and neural networks each offer complementary frameworks for prediction. Theoretically grounded in inference, mixed models can unveil latent variance structure notably with the factor‐analytic approach. In this article, we propose to bridge the gap between the two frameworks, leveraging embedding data from a neural network encoder ...
Alexandre Marchal +3 more
wiley +1 more source
Conjugate cone characterization of positive definite and semidefinite matrices [PDF]
, 1984 Positive definite and semidefinite matrices are characterized in terms of positive definiteness and semidefiniteness on arbitrary closed convex cones in Rn.
Mangasarian, O.L., Han, S.-P.
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