Results 31 to 40 of about 670,467 (323)
Experience with a Matrix Norm Estimator [PDF]
SIAM Journal on Scientific and Statistical Computing, 1990 Summary: Fortran 77 codes for estimating the 1-norm of a real or complex matrix were presented by the author [ACM Trans. Math. Software 14, No. 4, 381- 396 (1988; Zbl 0665.65043)]. The codes have found use in various applications and have been adopted by two program libraries.
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Matrix completion via modified schatten 2/3-norm
EURASIP Journal on Advances in Signal Processing, 2023 Low-rank matrix completion is a hot topic in the field of machine learning. It is widely used in image processing, recommendation systems and subspace clustering.
Jincai Ha +3 more
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Improving compressed sensing with the diamond norm
, 2016 In low-rank matrix recovery, one aims to reconstruct a low-rank matrix from a minimal number of linear measurements. Within the paradigm of compressed sensing, this is made computationally efficient by minimizing the nuclear norm as a convex surrogate ...
Eisert, Jens +3 more
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Sum of squared logarithms - An inequality relating positive definite matrices and their matrix logarithm [PDF]
, 2013 Let y1, y2, y3, a1, a2, a3 > 0 be such that y1 y2 y3 = a1 a2 a3 and y1 + y2 + y3 >= a1 + a2 + a3, y1 y2 + y2 y3 + y1 y3 >= a1 a2 + a2 a3 + a1 a3. Then the following inequality holds (log y1)^2 + (log y2)^2 + (log y3)^2 >= (log a1)^2 + (log a2)^2 + (log
Birsan, Mircea +2 more
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Matrix Completions, Norms, and Hadamard Products [PDF]
Proceedings of the American Mathematical Society, 1993 The author obtains a necessary and sufficient condition for \(X_ 0\in S\subset H_ n\) to attain the maximum in the problem \(\max\{\lambda_{\min}(A+X):X\in S\}\), where \(A\in H_ n\) is a fixed matrix, \(H_ n\) is the space of \(n\times n\) Hermitian matrices, and \(S\) is a closed convex set.
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An Iterative Algorithm for the Reflexive Solution of the General Coupled Matrix Equations
The Scientific World Journal, 2013 The general coupled matrix equations (including the generalized coupled Sylvester matrix equations as special cases) have numerous applications in control and system theory.
Zhongli Zhou, Guangxin Huang
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On the spectral and Frobenius norm of a generalized Fibonacci r-circulant matrix
Special Matrices, 2018 Consider the recursion g0 = a, g1 = b, gn = gn−1 + gn−2, n = 2, 3, . . . . We compute the Frobenius norm of the r-circulant matrix corresponding to g0, . . . , gn−1. We also give three lower bounds (with equality conditions) for the spectral norm of this
Merikoski Jorma K. +3 more
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On Skew Circulant Type Matrices Involving Any Continuous Fibonacci Numbers
Abstract and Applied Analysis, 2014 Circulant and skew circulant matrices have become an important tool in networks engineering. In this paper, we consider skew circulant type matrices with any continuous Fibonacci numbers.
Zhaolin Jiang, Jinjiang Yao, Fuliang Lu
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Double Structured Nuclear Norm-Based Matrix Decomposition for Saliency Detection
IEEE Access, 2020 Saliency detection aims at identifying the most important and informative area in a scene. Recently low rank matrix recovery (LR) theory becomes an effective tool for saliency detection.
Junxia Li, Ziyang Wang, Zefeng Pan
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Weighted Schatten p-Norm Low Rank Error Constraint for Image Denoising
Entropy, 2021 Traditional image denoising algorithms obtain prior information from noisy images that are directly based on low rank matrix restoration, which pays little attention to the nonlocal self-similarity errors between clear images and noisy images. This paper
Jiucheng Xu, Yihao Cheng, Yuanyuan Ma
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