Adaptive Decentralized Resource Optimization
Abstract
We propose a new paradigm for distributed resource optimization which is considerably more general than allpreviously proposed models and captures the complexity inherent in military operations: time-dependent objectives, local as well as global constraints, couplings between neighboring nodes. Our model is motivated by problems of positioning for optimal monitoring, fusion of disparate types of data in sensor networks, and adaptive routing in search and-rescue scenarios. We will develop algorithms for our distributed optimization model, and by extensions for all of the above problems, which are fully distributed, scale favorably in the number of nodes, and exhibit fast geometric convergence. Furthermore, we will develop techniques that will allow for switching adaptively between distributed and centralized protocols. Moreover, we will deliver codes implementing our methods that will be accompanied by transparent guarantees on performance
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 03, 2017
- Source ID
- N000141712195
Entities
People
- Alexander Olshevsky
Organizations
- Boston University
- Office of Naval Research
- United States Navy