Fast-Lipschitz optimization with wireless sensor networks applications
2011 (English)In: Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN'11, 2011, 378-389 p.Conference paper (Refereed)
Motivated by the need for fast computations demanded by wireless sensor networks, the new F-Lipschitz optimization theory is introduced for a novel class of optimization problems. These problems are defined by simple qualifying properties specified in terms of increasing objective function and contractive constraints. It is shown that feasible F-Lipschitz problems have always a unique optimal solution that satisfies the constraints at equality. The solution is obtained quickly by asynchronous algorithms of certified convergence. F-Lipschitz optimization can be applied to both centralized and distributed optimization. Compared to traditional Lagrangian methods, which often converge linearly, the convergence time of centralized F-Lipschitz problems is at least superlinear. Distributed F-Lipschitz algorithms converge fast, as opposed to traditional La-grangian decomposition and parallelization methods, which generally converge slowly and at the price of many message passings. In both cases, the computational complexity is much lower than traditional Lagrangian methods. Examples of application of the new optimization method are given for distributed detection and radio power control in wireless sensor networks. The drawback of the F-Lipschitz optimization is that it might be difficult to check the qualifying properties. For more general optimization problems, it is suggested that it is convenient to have conditions ensuring that the solution satisfies the constraints at equality.
Place, publisher, year, edition, pages
2011. 378-389 p.
, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN'11
Convex and Non-convex Optimization, Distributed Optimization, Interference Function Theory, Parallel and Distributed Computation, Wireless Sensor Networks, Distributed computations, Nonconvex optimization, Wireless sensor, Algorithms, Computational complexity, Convergence of numerical methods, Convex optimization, Data processing, Lagrange multipliers, Optimization, Sensors, Site selection
Control Engineering Computational Mathematics
IdentifiersURN: urn:nbn:se:kth:diva-151202ScopusID: 2-s2.0-79959310499ISBN: 9781612848549OAI: oai:DiVA.org:kth-151202DiVA: diva2:748444
10th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN'11, 12 April 2011 through 14 April 2011, Chicago, IL
QC 201409192014-09-192014-09-152014-09-19Bibliographically approved