Conference proceeding
Global Uniform Asymptotic Stability of a Generalized Adaptive Bellman-Ford Algorithm
2019 IEEE 58th Conference on Decision and Control (CDC), Vol.2019-, pp.1868-1873
12/2019
DOI: 10.1109/CDC40024.2019.9029773
Abstract
Self-stabilizing information spreading algorithms are a key basis block for building distributed system for device coordination. The adaptive Bellman-Ford (ABF) algorithm is a special case of these spreading algorithms. It finds the distance estimate of each node in a graph from a source set, but unlike the classical Bellman-Ford algorithm does not assume that all initial distance estimates exceed their true values. Though globally uniformly asymptotically stable (GUAS), its convergence can be very slow in graphs will short edges if some initial estimates are smaller than their true values. We propose here a generalization of ABF with additional parameters to permit faster convergence. We prove it to be GUAS, bounding the time to converge, and show via simulations that it withstands persistent bounded perturbations in the graph edge lengths.
Details
- Title: Subtitle
- Global Uniform Asymptotic Stability of a Generalized Adaptive Bellman-Ford Algorithm
- Creators
- Yuanqiu Mo - Westlake UniversitySoura Dasgupta - University of IowaJacob Beal - BBN Technologies
- Resource Type
- Conference proceeding
- Publication Details
- 2019 IEEE 58th Conference on Decision and Control (CDC), Vol.2019-, pp.1868-1873
- Publisher
- IEEE
- DOI
- 10.1109/CDC40024.2019.9029773
- ISSN
- 0743-1546
- eISSN
- 2576-2370
- Language
- English
- Date published
- 12/2019
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197527602771
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