中国科学技术大学：Decentralized Jointly Sparse Optimization by Reweighted Lq Minimization

Decentralized Jointly Sparse Optimization by Reweighted Lq Minimization Qing Ling Department of Automation University of Science and Technology of China Joint work with Zaiwen Wen(SJTU)and Wotao Yin(RICE) 2012/09/05 1

1 Decentralized Jointly Sparse Optimization by Reweighted Lq Minimization Qing Ling Department of Automation University of Science and Technology of China Joint work with Zaiwen Wen (SJTU) and Wotao Yin (RICE) 2012/09/05

A brief introduction to my research interest optimization and control in networked multi-agent systems autonomous agents collect data process data Sodo 回图包 communicate problem:how to efficiently accomplish in-network optimization and control tasks through collaboration of agents? 2

2 A brief introduction to my research interest optimization and control in networked multi-agent systems autonomous agents - collect data - process data - communicate problem: how to efficiently accomplish in-network optimization and control tasks through collaboration of agents?

Large-scale wireless sensor networks:decentralized signal processing,node localization,sensor selection blind anchor how to localize blinds with anchors? how to fuse big sensory data? e.g.structural health monitoring difficulty in data transmission → decentralized optimization without any fusion center how to assign sensors to targets? 3

3 Large-scale wireless sensor networks: decentralized signal processing, node localization, sensor selection … how to fuse big sensory data? e.g. structural health monitoring how to localize blinds with anchors? blind anchor how to assign sensors to targets? difficulty in data transmission → decentralized optimization without any fusion center

Computer/server networks with big data:collaborative data mining new challenges in the big data era big data is stored in distributed computers/servers data transmission is prohibited due to bandwidth/privacy/... computers/servers collaborate to do data mining distributed/decentralized optimization 4

4 Computer/server networks with big data: collaborative data mining new challenges in the big data era - big data is stored in distributed computers/servers - data transmission is prohibited due to bandwidth/privacy/… - computers/servers collaborate to do data mining distributed/decentralized optimization

Wireless sensor and actuator networks:with application in large-scale greenhouse control wireless sensing temperature humidity wireless actuating circulating fan wet curtain disadvantages of traditional centralized control communication burden in collecting distributed sensory data lack of robustness due to packet-loss,time-delay,.. decentralized control system design 5

5 Wireless sensor and actuator networks: with application in large-scale greenhouse control decentralized control system design wireless sensing - temperature - humidity - … wireless actuating - circulating fan - wet curtain - … disadvantages of traditional centralized control - communication burden in collecting distributed sensory data - lack of robustness due to packet-loss, time-delay, …

Recent works wireless sensor networks decentralized signal processing with application in SHM decentralized node localization using SDP and SOCP decentralized sensor node selection for target tracking collaborative data mining decentralized approaches to jointly sparse signal recovery decentralized approaches to matrix completion wireless sensor and actuator networks modeling,hardware design,controller design,prototype theoretical issues convergence and convergence rate analysis 6

6 Recent works wireless sensor networks - decentralized signal processing with application in SHM - decentralized node localization using SDP and SOCP - decentralized sensor node selection for target tracking collaborative data mining - decentralized approaches to jointly sparse signal recovery - decentralized approaches to matrix completion wireless sensor and actuator networks - modeling, hardware design, controller design, prototype theoretical issues - convergence and convergence rate analysis

Decentralized Jointly Sparse Optimization by Reweighted Lq Minimization Qing Ling Department of Automation University of Science and Technology of China Joint work with Zaiwen Wen(SJTU)and Wotao Yin(RICE) 2012/09/05 7

7 Decentralized Jointly Sparse Optimization by Reweighted Lq Minimization Qing Ling Department of Automation University of Science and Technology of China Joint work with Zaiwen Wen (SJTU) and Wotao Yin (RICE) 2012/09/05

Outline Background decentralized jointly sparse optimization with applications Roadmap nonconvex versus convex,difficulty in decentralized computing Algorithm development successive linearization,inexact average consensus Simulation and conclusion 8

8 Outline ❑ Background ❑ decentralized jointly sparse optimization with applications ❑ Roadmap ❑ nonconvex versus convex, difficulty in decentralized computing ❑ Algorithm development ❑ successive linearization, inexact average consensus ❑ Simulation and conclusion

Background(I):jointly sparse optimization Structured signals A sparse signal:only few elements are nonzero Jointly sparse signals:sparse,with the same nonzero supports zeros x(⊙∈RN nonzeros X=x四)x②).…x(] Jointly sparse optimization:to recover X from linear measurements y@=A@)x+e(∈RM,A)∈RMxN,e国∈RM ↓ measurement matrix measurement noise 9

9 Background (I): jointly sparse optimization ◼ Structured signals ⚫ A sparse signal: only few elements are nonzero ⚫ Jointly sparse signals: sparse, with the same nonzero supports ◼ Jointly sparse optimization: to recover X from linear measurements nonzeros zeros measurement matrix measurement noise

Background (II):decentralized jointly sparse optimization Decentralized computing in a network Distributed data in distributed agents no fusion center e Consideration of privacy,difficulty in data collection,etc Decentralized jointly sparse optimization Goal:agent i has y(i)and A(i),to recover x(i)through collaboration 10

10 Background (II): decentralized jointly sparse optimization ◼ Decentralized computing in a network ⚫ Distributed data in distributed agents & no fusion center ⚫ Consideration of privacy, difficulty in data collection, etc Goal: agent i has y(i) and A(i), to recover x(i) through collaboration ◼ Decentralized jointly sparse optimization