scholarly journals Successive Ray Refinement and Its Application to Coordinate Descent for Lasso

2016 ◽  
pp. 310-320
Author(s):  
Jun Liu ◽  
Zheng Zhao ◽  
Ruiwen Zhang ◽  
Yan Xu
Keyword(s):  
Coordinate Descent

Efficient Detection for Large-Scale MIMO Systems Using Dichotomous Coordinate Descent Iterations

2020 ◽  
Vol E103.B (11) ◽  
pp. 1310-1317
Author(s):  
Zhi QUAN ◽  
Shuhua LV ◽  
Li JIANG
Keyword(s):  
Large Scale ◽  
Mimo Systems ◽  
Coordinate Descent ◽  
Efficient Detection

scholarly journals Efficient Asynchronous Semi-stochastic Block Coordinate Descent Methods for Large-Scale SVD

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Fanhua Shang ◽  
Zhihui Zhang ◽  
Yuanyuan Liu ◽  
Hongying Liua ◽  
Jing Xu
Keyword(s):  
Large Scale ◽  
Coordinate Descent ◽  
Descent Methods ◽  
Block Coordinate Descent

scholarly journals Differentiable Architecture Search Based on Coordinate Descent

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 48544-48554
Author(s):  
Pyunghwan Ahn ◽  
Hyeong Gwon Hong ◽  
Junmo Kim
Keyword(s):  
Coordinate Descent

scholarly journals A Block Coordinate Descent-Based Projected Gradient Algorithm for Orthogonal Non-Negative Matrix Factorization

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 540
Author(s):  
Soodabeh Asadi ◽  
Janez Povh
Keyword(s):  
Matrix Factorization ◽  
Synthetic Data ◽  
Coordinate Descent ◽  
The Other ◽  
Gradient Algorithm ◽  
Block Coordinate Descent ◽  
Projected Gradient Method ◽  
Projected Gradient ◽  
Factorization Problem ◽  
Non Negative Matrix Factorization

This article uses the projected gradient method (PG) for a non-negative matrix factorization problem (NMF), where one or both matrix factors must have orthonormal columns or rows. We penalize the orthonormality constraints and apply the PG method via a block coordinate descent approach. This means that at a certain time one matrix factor is fixed and the other is updated by moving along the steepest descent direction computed from the penalized objective function and projecting onto the space of non-negative matrices. Our method is tested on two sets of synthetic data for various values of penalty parameters. The performance is compared to the well-known multiplicative update (MU) method from Ding (2006), and with a modified global convergent variant of the MU algorithm recently proposed by Mirzal (2014). We provide extensive numerical results coupled with appropriate visualizations, which demonstrate that our method is very competitive and usually outperforms the other two methods.

Population-based coordinate descent algorithm with majority voting

2021 ◽  
Author(s):  
Davood Zaman Farsa ◽  
Azam Asilian Bidgoli ◽  
Ehsan Rokhsat-Yazdi ◽  
Shahryar Rahnamayan
Keyword(s):  
Population Based ◽  
Coordinate Descent ◽  
Majority Voting ◽  
Descent Algorithm ◽  
Coordinate Descent Algorithm

Coordinate Descent Method for k-means

2021 ◽  
pp. 1-1
Author(s):  
Feiping Nie ◽  
Jingjing Xue ◽  
Danyang Wu ◽  
Rong Wang ◽  
Hui Li ◽  
...  
Keyword(s):  
Descent Method ◽  
Coordinate Descent ◽  
Coordinate Descent Method

scholarly journals A Fast Active Set Block Coordinate Descent Algorithm for $\ell_1$-Regularized Least Squares

2016 ◽  
Vol 26 (1) ◽  
pp. 781-809 ◽  
Author(s):  
Marianna De Santis ◽  
Stefano Lucidi ◽  
Francesco Rinaldi
Keyword(s):  
Least Squares ◽  
Coordinate Descent ◽  
Block Coordinate Descent ◽  
Active Set ◽  
Descent Algorithm ◽  
Regularized Least Squares ◽  
Coordinate Descent Algorithm

scholarly journals FPGA design of a coordinate descent data detector for large-scale MU-MIMO

2016 ◽  
Author(s):  
Michael Wu ◽  
Chris Dick ◽  
Joseph R. Cavallaro ◽  
Christoph Studer
Keyword(s):  
Large Scale ◽  
Coordinate Descent ◽  
Fpga Design ◽  
Descent Data

scholarly journals Coordinate descent with arbitrary sampling II: expected separable overapproximation

2016 ◽  
Vol 31 (5) ◽  
pp. 858-884 ◽  
Author(s):  
Zheng Qu ◽  
Peter Richtárik
Keyword(s):  
Coordinate Descent
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