Approximate Matrix and Tensor Diagonalization by Unitary Transformations: Convergence of Jacobi-Type Algorithms [PDF]
SIAM Journal on Optimization, 2020 We propose a gradient-based Jacobi algorithm for a class of maximization problems on the unitary group, with a focus on approximate diagonalization of complex matrices and tensors by unitary transformations. We provide weak convergence results, and prove local linear convergence of this algorithm.The convergence results also apply to the case of real ...
Usevich, Konstantin +2 more
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Performance analysis of beamformers using generalized loading of the covariance matrix in the presence of random steering vector errors [PDF]
, 2005 Robust adaptive beamforming is a key issue in array applications where there exist uncertainties about the steering vector of interest. Diagonal loading is one of the most popular techniques to improve robustness.
Besson, Olivier, Vincent, François
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Diagonal Hessian Approximation for Limited Memory Quasi-Newton via Variational Principle
Journal of Applied Mathematics, 2013 This paper proposes some diagonal matrices that approximate the (inverse) Hessian by parts using the variational principle that is analogous to the one employed in constructing quasi-Newton updates. The way we derive our approximations is inspired by the
Siti Mahani Marjugi, Wah June Leong
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Reconstruction of sparse check matrix for LDPC at high bit error rate
Tongxin xuebao, 2021 In order to reconstruct the sparse check matrix of LDPC, a new algorithm which could directly reconstruct the LDPC was proposed.Firstly, according to the principle of the traditional reconstruction algorithm, the defects of the traditional algorithm and ...
Zhaojun WU +3 more
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Sparse Graph Learning Under Laplacian-Related Constraints
IEEE Access, 2021 We consider the problem of learning a sparse undirected graph underlying a given set of multivariate data. We focus on graph Laplacian-related constraints on the sparse precision matrix that encodes conditional dependence between the random variables ...
Jitendra K. Tugnait
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A Singular Value Thresholding with Diagonal-Update Algorithm for Low-Rank Matrix Completion [PDF]
Mathematical Problems in Engineering, 2020 The singular value thresholding (SVT) algorithm plays an important role in the well-known matrix reconstruction problem, and it has many applications in computer vision and recommendation systems. In this paper, an SVT with diagonal-update (D-SVT) algorithm was put forward, which allows the algorithm to make use of simple arithmetic operation and keep ...
Yong-Hong Duan, Rui-Ping Wen, Yun Xiao
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Knowledge-aided STAP in heterogeneous clutter using a hierarchical bayesian algorithm [PDF]
, 2011 This paper addresses the problem of estimating the covariance matrix of a primary vector from heterogeneous samples and some prior knowledge, under the framework of knowledge-aided space-time adaptive processing (KA-STAP).
Bidon, Stéphanie +5 more
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A Diagonalization Algorithm for the Distance Matrix of Cographs
IEEE Access, 2018 Cographs is a well-known class of graphs in graph theory, which can be generated from a single vertex by applying a series of complement (or equivalently join operations) and disjoint union operations. The distance spectrum of graphs is a rather active topic in spectral graph theory these years.
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Approximate diagonalization in differential systems and an effective algorithm for the computation of the spectral matrix [PDF]
Mathematical Proceedings of the Cambridge Philosophical Society, 1997 This paper reports on recent work to compute the asymptotic solution of a n-th order ordinary differential equation. Symbolic methods are used to compute the asymptotics over a large region. Application is made to the computation of the Titchmarsh-Weyl M-matrix for the fourth order operator.
Brown, B. M. +3 more
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Performance analysis for a class of robust adaptive beamformers [PDF]
, 2004 Robust adaptive beamforming is a key issue in array applications where there exist uncertainties about the steering vector of interest. Diagonal loading is one of the most popular techniques to improve robustness.
O. Besson +3 more
core +1 more source