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Subspace Methods for Nonlinear Optimization
CSIAM Transactions on Applied Mathematics, 2021 Summary: Subspace techniques such as Krylov subspace methods have been well known and extensively used in numerical linear algebra. They are also ubiquitous and becoming indispensable tools in nonlinear optimization due to their ability to handle large scale problems. There are generally two types of principals: i) the decision variable is updated in a
Liu, Xin, Wen, Zaiwen, Yuan, Ya-Xiang
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Anomaly Community Detection Method via Subspace Combining Node Attribute and Structure Information [PDF]
Jisuanji gongcheng, 2020 This paper proposes an anomaly community detection method via subspace by combining node attributes with structure information.First,in the given set of to-be-tested communities,the subspace solution strategy based on the average distance of attributes ...
ZHAO Qiqi, MA Huifang, LIU Haijiao, JIA Junjie
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Robust auto-weighted multi-view subspace clustering with common subspace representation matrix. [PDF]
PLoS ONE, 2017 In many computer vision and machine learning applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is a powerful technology to find the underlying subspaces and cluster data points correctly.
Wenzhang Zhuge +5 more
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KRYLOV SUBSPACE METHODS FOR SOLVING LARGE LYAPUNOV EQUATIONS [PDF]
, 1994 Published ...
KASENALLY, EM, JAIMOUKHA, IM
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Comparative analysis of power spectrum estimation methods
Lietuvos Matematikos Rinkinys, 2010 The subject of this paper is the comparative analysis of the eleven most important nonparametric, parametric and subspace power spectrum estimation methods.
Gintarė Petreikytė, Kazys Kazlauskas
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On the Preconditioned Delayed Weighted Gradient Method
Trends in Computational and Applied Mathematics, 2023 In this article a preconditioned version of the Delayed Weighted Gradient Method (DWGM) is presented and analyzed. In addition to the convergence, some nice properties as the A- orthogonality of the current transformed gradient with all the previous ...
R. Aleixo, H. Lara Urdaneta
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Minimum mean square distance estimation of a subspace [PDF]
, 2011 We consider the problem of subspace estimation in a Bayesian setting. Since we are operating in the Grassmann manifold, the usual approach which consists of minimizing the mean square error (MSE) between the true subspace U and its estimate U may not be ...
Jean-Yves Tourneret +5 more
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Detection of a signal in linear subspace with bounded mismatch [PDF]
, 2006 We consider the problem of detecting a signal of interest in a background of noise with unknown covariance matrix, taking into account a possible mismatch between the actual steering vector and the presumed one.
Besson, Olivier
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Subspace System Identification of the Kalman Filter [PDF]
Modeling, Identification and Control, 2003 Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati ...
David Di Ruscio
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Preconditioners for Krylov subspace methods: An overview [PDF]
GAMM-Mitteilungen, 2020 AbstractWhen simulating a mechanism from science or engineering, or an industrial process, one is frequently required to construct a mathematical model, and then resolve this model numerically. If accurate numerical solutions are necessary or desirable, this can involve solving large‐scale systems of equations.
Pearson, John W., Pestana, Jennifer
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