Finding nonlinear system equations and complex network structures from data: A sparse optimization approach

Ying-Cheng Lai

doi:10.1063/5.0062042

Finding nonlinear system equations and complex network structures from data: A sparse optimization approach

Chaos An Interdisciplinary Journal of Nonlinear Science ◽

10.1063/5.0062042 ◽

2021 ◽

Vol 31 (8) ◽

pp. 082101 ◽

Cited By ~ 1

Author(s):

Ying-Cheng Lai

Keyword(s):

Nonlinear System ◽

Complex Network ◽

Sparse Optimization ◽

Optimization Approach ◽

Network Structures ◽

System Equations

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Evolutionary algorithms dynamics and its hidden complex network structures

2014 IEEE Congress on Evolutionary Computation (CEC) ◽

10.1109/cec.2014.6900441 ◽

2014 ◽

Cited By ~ 26

Author(s):

Ivan Zelinka ◽

Donald Davendra ◽

Jouni Lampinen ◽

Roman Senkerik ◽

Michal Pluhacek

Keyword(s):

Evolutionary Algorithms ◽

Complex Network ◽

Network Structures

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

Journal of Advanced Transportation ◽

10.1155/2018/1784789 ◽

2018 ◽

Vol 2018 ◽

pp. 1-19 ◽

Cited By ~ 1

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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