scholarly journals Sparse optimization of mutual synchronization in collectively oscillating networks

2021 ◽  
Vol 31 (6) ◽  
pp. 063113
Author(s):  
Hiroya Nakao ◽  
Katsunori Yamaguchi ◽  
Shingo Katayama ◽  
Tatsuo Yanagita
Keyword(s):  
Sparse Optimization ◽  
Mutual Synchronization

Mutual Synchronization with 24 GHz Oscillators

2021 ◽  
Author(s):  
C. Hoyer ◽  
D. Prousalis ◽  
L. Wetzel ◽  
R. Riaz ◽  
Jens Wagner ◽  
...  
Keyword(s):  
Mutual Synchronization ◽  
24 Ghz

Convolutional plug-and-play sparse optimization for impulsive blind deconvolution

2021 ◽  
Vol 161 ◽  
pp. 107877
Author(s):  
Zhaohui Du ◽  
Han Zhang ◽  
Xuefeng Chen ◽  
Yixin Yang
Keyword(s):  
Blind Deconvolution ◽  
Sparse Optimization ◽  
Plug And Play

Mutual Synchronization of Passive Hydrogen Masers

2021 ◽  
Author(s):  
Konstantin Mishagin ◽  
Artem Pelyushenko
Keyword(s):  
Mutual Synchronization

scholarly journals A Sparse Optimization Approach for Energy-Efficient Timetabling in Metro Railway Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Ziyan Luo ◽  
Xiaoyu Li ◽  
Naihua Xiu
Keyword(s):  
Optimization Model ◽  
Energy Efficient ◽  
Convex Relaxation ◽  
Sparse Optimization ◽  
Computational Time ◽  
Optimization Approach ◽  
Railway Systems ◽  
Alternating Direction ◽  
Np Hard Problem ◽  
Regenerative Energy

In this paper, we propose a sparse optimization approach to maximize the utilization of regenerative energy produced by braking trains for energy-efficient timetabling in metro railway systems. By introducing the cardinality function and the square of the Euclidean norm function as the objective function, the resulting sparse optimization model can characterize the utilization of the regenerative energy appropriately. A two-stage alternating direction method of multipliers is designed to efficiently solve the convex relaxation counterpart of the original NP-hard problem and then to produce an energy-efficient timetable of trains. The resulting approach is applied to Beijing Metro Yizhuang Line with different instances of service for case study. Comparison with the existing two-step linear program approach is also conducted which illustrates the effectiveness of our proposed sparse optimization model in terms of the energy saving rate and the efficiency of our numerical optimization algorithm in terms of computational time.

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