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

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?

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? blindanchor how to assign sensors to targets? difficulty in data transmission → decentralized optimization without any fusion center

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

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, …

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

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

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

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 matrixmeasurement noise

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

11. 11 Background (III): applications Cooperative spectrum sensing [1][2] Cognitive radios sense jointly sparse spectra {x (i) } Measure from time domain [1] or frequency selective filter [2] Decentralized recovery from {y (i) =A (i) x (i) } [1] F. Zeng, C. Li, and Z. Tian, “Distributed compressive spectrum sensing in cooperative multi-hop wideband cognitive networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 5, pp. 37–48, 2011 [2] J. Meng, W. Yin, H. Li, E. Houssain, and Z. Han, “Collaborative spectrum sensing from sparse observations for cognitive radio networks,” IEEE Journal on Selected Areas on Communications, vol. 29, pp. 327–337, 2011 [3] N. Nguyen, N. Nasrabadi, and T. Tran, “Robust multi-sensor classification via joint sparse representation,” submitted to Journal of Advance in Information Fusion Decentralized event detection [3] Sensors {i} sense few targets represented by jointly sparse {x (i) } Decentralized recovery from {y (i) =A (i) x (i) } Collaborative data mining, distributed human action recognition, etc

12. 12 Roadmap (I): nonconvex versus convex Convex model: group lasso or L 21 norm minimization Nonconvex versus convex Convex: with global convergence guarantee Nonconvex: often with better recovery performance Look back on nonconvex models to recover a single sparse signal Reweighted L 1 /L 2 norm minimization [4][5] Reweighted algorithms for jointly sparse optimization? [4] E. Candes, M. Wakin, and S. Boyd, “Enhancing sparsity by reweighted L1 minimization,” Journal of Fourier Analysis and Applications, vol. 14, pp. 877–905, 2008 [5] R. Chartrand and W. Yin, “Iteratively reweighted algorithms for compressive sensing,” In: Proceedings of ICASSP, 2008 regularization parameter

13. 13 Roadmap (II): difficulty in decentralized computing A popular decentralized computing technique: consensus common optimization variableobjective function in agent i local copy in agent ineighboring copies are equal Obviously, two problems are equivalent for a connected network Efficient algorithms (ADM, SGD, etc) for if it is convex [6] Nothing for consensus in jointly sparse optimization! Signals are different; common supports bring nonconvexity [6] D. Bertsekas and J. Tsitsiklis, Parallel and Distributed Computation: Numerical Methods, Second Edition, Athena Scientific, 1997

14. 14 Roadmap (III): solution overview Nonconvex model + convex decentralized computing subproblem Nonconvex model -> successive linearization -> reweighted Lq Natural decentralized computing, one nontrivial subproblem Inexactly solving the subproblem still leads to good recovery

15. 15 Algorithm (I): successive linearization Nonconvex model (q=1 or 2) regularization parameter smoothing parameter “Successive linearization” to the joint sparsity term at t Actually a majorization minimization approach

16. 16 Algorithm (II): reweighted algorithm Centralized reweighted Lq minimization algorithm Updating weight vector weight vector u=[u 1 ; u 2 ; u N ] Updating signals From a decentralized implementation perspective … Natural decentralized computing in x-update One subproblem needs decentralized solution in u-update

17. 17 Algorithm (III): average consensus Check u-update: average consensus problem Rewrite to more familiar forms

18. 18 Algorithm (IV): inexact average consensus Solve the average consensus problem with ADM (time t, slot s/S) Updating weight vectors (local copies) Updating Lagrange multipliers (c is a positive constant) Exact average consensus versus inexact average consensus Exact average consensus: exact implementation of reweighted Lq Introducing inner loops: cost of coordination & communication Inexact average consensus: one iteration in the inner loop

19. 19 Algorithm (V): decentralized reweighted Lq Algorithm outline Updating weight vectors (local copies) Updating Lagrange multipliers (c is a positive constant) Updating signals

20. 20 Simulation (I): simulation settings Network settings L=50 agents, randomly deployed in 100×100 area Communication range=30, bidirectionally connected Measurement settings Signal dimension N=20, signal sparsity K=2 Measurement dimension M=10 Random measurement matrices and random measurement noise Parameter settings

21. 21 Simulation (II): recovery performance

22. 22 Simulation (III): convergence rate

23. 23 Conclusion Decentralized jointly sparse optimization problem Jointly sparse signal recovery in a distributed network Reweighted Lq minimization algorithms Feature #1: nonconvex model <- successive linearization Feature #2: decentralized computing <- inexact average consensus Outlook: many open questions in decentralized optimization Good news and bad news Local convergence of the centralized algorithms Excellent performance of the decentralized algorithms No theoretical performance guarantee (recovery and convergence)

24. 24 Thanks for your attention!