Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Brian Swenson"'
Autor:
Frederick M. Heim, Pablo C. Bueno, Sidney Chocron, James D. Walker, Alexander Carpenter, Jon T. Cutshall, Brian Swenson, Theodore Bapty, Sydney Whittington
Publikováno v:
AIAA SCITECH 2023 Forum.
Autor:
James D. Walker, F. Michael Heim, Bapiraju Surampudi, Pablo Bueno, Alexander Carpenter, Sidney Chocron, Jon Cutshall, Richard Lammons, Theodore Bapty, Brian Swenson, Sydney Whittington
Publikováno v:
2022 IEEE Workshop on Design Automation for CPS and IoT (DESTION).
Autor:
Theodore Bapty, Sydney Whittington, James Walker, Joseph Hite, Brian Swenson, Katherine Owens, Fred Eisele, Jason Scott, Robert Owens
Publikováno v:
2022 IEEE Workshop on Design Automation for CPS and IoT (DESTION).
Publikováno v:
MobiHoc
Mobile crowd sensing (MCS) has been used to enable a wide range of resource-discovery applications by exploiting the "wisdom" of many mobile users. However, in many applications, a user's valuation depends on other users' sensory data, which introduc
The paper considers distributed gradient flow (DGF) for multi-agent nonconvex optimization. DGF is a continuous-time approximation of distributed gradient descent that is often easier to study than its discrete-time counterpart. The paper has two mai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f7ad418d694e1d3d0f588f224d8f2808
Autor:
H. Vincent Poor, Brian Swenson
Publikováno v:
ACSSC
The paper shows that smooth fictitious play converges to a neighborhood of a pure-strategy Nash equilibrium with probability 1 in almost all N × 2 (N-player, two-action) potential games. The neighborhood of convergence may be made arbitrarily small
Publikováno v:
ICASSP
The paper considers the problem of network-based computation of global minima in smooth nonconvex optimization problems. It is known that distributed gradient-descent-type algorithms can achieve convergence to the set of global minima by adding slowl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5615c1c6cb86b5b8ee3a46e039020ad8
http://arxiv.org/abs/1910.09587
http://arxiv.org/abs/1910.09587
Publikováno v:
IEEE Transactions on Automatic Control. 62:6026-6031
This paper is concerned with distributed learning and optimization in large-scale settings. The well-known fictitious play (FP) algorithm has been shown to achieve Nash equilibrium learning in certain classes of multiagent games. However, FP can be c
Publikováno v:
Allerton
The paper studies a distributed gradient descent (DGD) process and considers the problem of showing that in nonconvex optimization problems, DGD typically converges to local minima rather than saddle points. The paper considers unconstrained minimiza
Publikováno v:
ECC
This work studies a class of multi-player games in which the players' decisions can be influenced by a superplayer. We define a game with $n$ players and parameterized utilities $u$ (., a) where the superplayer controls the value of a. The regular pl