Results 1 to 10 of about 1,734,778 (295)
Low-rank factorization for rank minimization with nonconvex regularizers [PDF]
Computational Optimization and Applications, 2021 Rank minimization is of interest in machine learning applications such as recommender systems and robust principal component analysis. Minimizing the convex relaxation to the rank minimization problem, the nuclear norm, is an effective technique to solve the problem with strong performance guarantees. However, nonconvex relaxations have less estimation
April Sagan, John E. Mitchell 0001
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Minimal Rank Properties of Outer Inverses with Prescribed Range and Null Space
Mathematics, 2023 The purpose of this paper is to investigate solvability of systems of constrained matrix equations in the form of constrained minimization problems.
Dijana Mosić +2 more
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2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022 Multi-criteria decision-making often requires finding a small representative set from the database. A recently proposed method is the regret minimization set (RMS) query. RMS returns a size $r$ subset $S$ of dataset $D$ that minimizes the regret-ratio (the difference between the score of top-1 in $S$ and the score of top-1 in $D$, for any possible ...
Xingxing Xiao, Jianzhong Li 0001
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Minimal rank completions for overlapping blocks [PDF]
Linear Algebra and its Applications, 2021 11 pages, 1 figure, accepted in Linear Algebra and its ...
Ethan N. Epperly +2 more
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On the rank minimization problem [PDF]
Proceedings of the 2004 American Control Conference, 2004 After a brief overview of the problem of finding the extremal (minimum or maximum) rank positive semidefinite matrix subject to matrix inequalities, we identify a few new classes of such problems that can be efficiently solved. We then proceed to present an algorithm for solving the general class of rank minimization problems.
Yoonsoo Kim 0001, Mehran Mesbahi
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Algorithm for Finding Minimal Algebras of Binary Multioperations of Rank 3
Современные информационные технологии и IT-образование, 2020 The theory of discrete functions studies not only the everywhere defined functions of k-valued logic, but also functions that are not defined on all sets.
Sergey Todikov
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The maximal size of a minimal generating set
Forum of Mathematics, Sigma, 2023 A generating set for a finite group G is minimal if no proper subset generates G, and $m(G)$ denotes the maximal size of a minimal generating set for G. We prove a conjecture of Lucchini, Moscatiello and Spiga by showing that there exist $a,b>
Scott Harper
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Implicit Rank-Minimizing Autoencoder
CoRR, 2020 An important component of autoencoders is the method by which the information capacity of the latent representation is minimized or limited. In this work, the rank of the covariance matrix of the codes is implicitly minimized by relying on the fact that gradient descent learning in multi-layer linear networks leads to minimum-rank solutions.
Li Jing 0001, Jure Zbontar, Yann LeCun
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Interference Alignment as a Rank Constrained Rank Minimization [PDF]
IEEE Transactions on Signal Processing, 2010 We show that the maximization of the sum degrees-of-freedom for the static flat-fading multiple-input multiple-output (MIMO) interference channel is equivalent to a rank constrained rank minimization problem (RCRM), when the signal spaces span all available dimensions.
Dimitris S. Papailiopoulos +1 more
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BMC Nephrology, 2020 Background Minimal change disease (MCD) is one of the causes of idiopathic nephrotic syndrome in adults. The pathogenesis of proteinuria in MCD has not been fully understood.
Keisuke Horikoshi +12 more
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