CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-10-25
Cited: 0
Clicked: 6019
Rui Liu, Wei-chu Sun, Tao Hou, Chun-hong Hu, Lin-bo Qiao. Block coordinate descent with time perturbation for nonconvex nonsmooth problems in real-world studies[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(10): 1390-1403.
@article{title="Block coordinate descent with time perturbation for nonconvex nonsmooth problems in real-world studies",
author="Rui Liu, Wei-chu Sun, Tao Hou, Chun-hong Hu, Lin-bo Qiao",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="10",
pages="1390-1403",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900341"
}
%0 Journal Article
%T Block coordinate descent with time perturbation for nonconvex nonsmooth problems in real-world studies
%A Rui Liu
%A Wei-chu Sun
%A Tao Hou
%A Chun-hong Hu
%A Lin-bo Qiao
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 10
%P 1390-1403
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900341
TY - JOUR
T1 - Block coordinate descent with time perturbation for nonconvex nonsmooth problems in real-world studies
A1 - Rui Liu
A1 - Wei-chu Sun
A1 - Tao Hou
A1 - Chun-hong Hu
A1 - Lin-bo Qiao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 10
SP - 1390
EP - 1403
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900341
Abstract: The era of big data in healthcare is here, and this era will significantly improve medicine and especially oncology. However, traditional machine learning algorithms need to be promoted to solve such large-scale real-world problems due to a large amount of data that needs to be analyzed and the difficulty in solving problems with nonconvex nonlinear settings. We aim to minimize the composite of a smooth nonlinear function and a block-separable nonconvex function on a large number of block variables with inequality constraints. We propose a novel parallel first-order optimization method, called asynchronous block coordinate descent with time perturbation (ATP), which adopts a time perturbation technique that escapes from saddle points and sub-optimal local points. The details of the proposed method are presented with analyses of convergence and iteration complexity properties. Experiments conducted on real-world machine learning problems validate the efficacy of our proposed method. The experimental results demonstrate that time perturbation enables ATP to escape from saddle points and sub-optimal points, providing a promising way to handle nonconvex optimization problems with inequality constraints employing asynchronous block coordinate descent. The asynchronous parallel implementation on shared memory multi-core platforms indicates that the proposed algorithm, ATP, has strong scalability.
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