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Published in 2020 at "Computational Optimization and Applications"
DOI: 10.1007/s10589-019-00137-2
Abstract: We propose new restarting strategies for the accelerated coordinate descent method. Our main contribution is to show that for a well chosen sequence of restarting times, the restarted method has a nearly geometric rate of…
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Keywords:
coordinate descent;
restarting accelerated;
method;
accelerated coordinate ... See more keywords
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Published in 2017 at "Journal of Optimization Theory and Applications"
DOI: 10.1007/s10957-016-1058-z
Abstract: In this paper we develop random block coordinate descent methods for minimizing large-scale linearly constrained convex problems over networks. Since coupled constraints appear in the problem, we devise an algorithm that updates in parallel at…
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Keywords:
linearly constrained;
descent methods;
coordinate descent;
random block ... See more keywords
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Published in 2022 at "IEEE journal of biomedical and health informatics"
DOI: 10.1109/jbhi.2022.3140455
Abstract: Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating. Nowadays, enhancement on medical diagnosis via machine learning models has been highly effective in many aspects of e-health…
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Keywords:
health analytics;
block coordinate;
health;
coordinate descent ... See more keywords
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Published in 2025 at "IEEE Journal on Selected Areas in Communications"
DOI: 10.1109/jsac.2025.3531558
Abstract: We present a 22nm FD-SOI (fully depleted silicon-on-insulator) application-specific integrated circuit (ASIC) implementation of a novel soft-output Gram-domain block coordinate descent (GBCD) data detector for massive multi-user (MU) multiple-input multiple-output (MIMO) systems. The ASIC simultaneously…
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Keywords:
detector asic;
coordinate descent;
deep unfolding;
data detector ... See more keywords
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Published in 2020 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2020.3028499
Abstract: The auxiliary normal equation is proposed to construct an incremental update (IU) system in which the increment of weight vector rather than the weight itself is optimized at each iteration, which however, can only deal…
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Keywords:
random fourier;
fourier features;
algorithm;
dichotomous coordinate ... See more keywords
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Published in 2022 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2021.3132276
Abstract: In this paper, we describe a new algorithm to build a few sparse principal components from a given data matrix. Our approach does not explicitly create the covariance matrix of the data and can be…
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Keywords:
principal components;
iterative coordinate;
algorithm compute;
descent algorithm ... See more keywords
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Published in 2022 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2020.3000905
Abstract: Dual Coordinate Descent (DCD) is one of the most popular optimization methods. The parallelization of DCD is difficult, as DCD is sequential in nature. As such, simultaneously running multiple DCD threads on batches of data…
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Keywords:
ppd;
coordinate descent;
parallel primal;
primal dual ... See more keywords
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Published in 2024 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2024.3373532
Abstract: Recently, more and more real-world datasets have been composed of heterogeneous but related features from diverse views. Multiview clustering provides a promising attempt at a solution for partitioning such data according to heterogeneous information. However,…
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Keywords:
free multiview;
method;
coordinate descent;
parameter free ... See more keywords
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Published in 2018 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2017.2757903
Abstract: This paper examines the mean-square error performance of diffusion stochastic algorithms under a generalized coordinate-descent scheme. In this setting, the adaptation step by each agent is limited to a random subset of the coordinates of…
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Keywords:
diffusion;
coordinate descent;
descent diffusion;
learning networked ... See more keywords
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Published in 2024 at "Symmetry"
DOI: 10.3390/sym17010012
Abstract: Research on the fairness of spectral clustering has gradually increased attention. Normally, existing methods of fair spectral clustering add a fairness constraint to the original objective function so that fairness is guaranteed. However, similar to…
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Keywords:
coordinate descent;
fairness;
spectral clustering;
fair spectral ... See more keywords