Imperial College London
Portrait Picture

Prof David Angeli

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Nonlinear Network Dynamics
 
 
 
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Contact

 

+44 (0)20 7594 6283d.angeli Website

 
 
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Location

 

1107CElectrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Angeli:2023:10.1016/j.automatica.2023.110870,
author = {Angeli, D and Manfredi, S},
doi = {10.1016/j.automatica.2023.110870},
journal = {Automatica},
pages = {1--9},
title = {Gradient-based local formulations of the Vickrey-Clarke-Groves mechanism for truthful minimization of social convex objectives},
url = {http://dx.doi.org/10.1016/j.automatica.2023.110870},
volume = {150},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We propose a gradient-based iterative method yielding a truthfulness preserving implementation of the Vickrey–Clarke–Groves mechanism for minimization of social convex objectives. The approach is guaranteed to return, in the limit, the same efficient outcomes of the VCG method, while improving its privacy limitations and reducing its communication requirements. Its performance is investigated through an illustrative example of vehicles coordination.
AU  - Angeli,D
AU  - Manfredi,S
DO  - 10.1016/j.automatica.2023.110870
EP  - 9
PY  - 2023///
SN  - 0005-1098
SP  - 1
TI  - Gradient-based local formulations of the Vickrey-Clarke-Groves mechanism for truthful minimization of social convex objectives
T2  - Automatica
UR  - http://dx.doi.org/10.1016/j.automatica.2023.110870
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000930837800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.sciencedirect.com/science/article/pii/S0005109823000201?via%3Dihub
UR  - http://hdl.handle.net/10044/1/103293
VL  - 150
ER  -
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