no code implementations • 26 Mar 2024 • Sarper Aydin, Orhan Eren Akgün, Stephanie Gil, Angelia Nedić
In this work, we consider the consensus problem in which legitimate agents share their values over an undirected communication network in the presence of malicious or faulty agents.
no code implementations • 9 Mar 2021 • Michal Yemini, Angelia Nedić, Andrea Goldsmith, Stephanie Gil
Further, the expected convergence rate decays exponentially with the quality of the trust observations between agents.
Optimization and Control Robotics Systems and Control Signal Processing Systems and Control
no code implementations • 12 Sep 2020 • Ran Xin, Shi Pu, Angelia Nedić, Usman A. Khan
Decentralized optimization to minimize a finite sum of functions over a network of nodes has been a significant focus within control and signal processing research due to its natural relevance to optimal control and signal estimation problems.
no code implementations • 4 Feb 2020 • Parth Thaker, Gautam Dasarathy, Angelia Nedić
We consider the problem of recovering a complex vector $\mathbf{x}\in \mathbb{C}^n$ from $m$ quadratic measurements $\{\langle A_i\mathbf{x}, \mathbf{x}\rangle\}_{i=1}^m$.
no code implementations • 3 Sep 2018 • César A. Uribe, Soomin Lee, Alexander Gasnikov, Angelia Nedić
Then, we study distributed optimization algorithms for non-dual friendly functions, as well as a method to improve the dependency on the parameters of the functions involved.
no code implementations • 25 May 2018 • Shi Pu, Angelia Nedić
In this paper, we study the problem of distributed multi-agent optimization over a network, where each agent possesses a local cost function that is smooth and strongly convex.
no code implementations • 21 Mar 2018 • Shi Pu, Angelia Nedić
In this paper, we study the problem of distributed multi-agent optimization over a network, where each agent possesses a local cost function that is smooth and strongly convex.
Optimization and Control Distributed, Parallel, and Cluster Computing Multiagent Systems
no code implementations • 8 Mar 2018 • César A. Uribe, Darina Dvinskikh, Pavel Dvurechensky, Alexander Gasnikov, Angelia Nedić
We propose a new \cu{class-optimal} algorithm for the distributed computation of Wasserstein Barycenters over networks.
no code implementations • 1 Dec 2017 • César A. Uribe, Soomin Lee, Alexander Gasnikov, Angelia Nedić
In this paper, we study the optimal convergence rate for distributed convex optimization problems in networks.
no code implementations • 16 Oct 2017 • Farzad Yousefian, Angelia Nedić, Uday Shanbhag
To the best of our knowledge, no rate statements currently exist for SQN methods in the absence of such an assumption.
Optimization and Control
no code implementations • 26 Sep 2017 • Angelia Nedić, Alex Olshevsky, Michael G. Rabbat
In decentralized optimization, nodes cooperate to minimize an overall objective function that is the sum (or average) of per-node private objective functions.
Optimization and Control Distributed, Parallel, and Cluster Computing Multiagent Systems
no code implementations • 10 Apr 2017 • Angelia Nedić, Alex Olshevsky, César A. Uribe
We study the problem of cooperative inference where a group of agents interact over a network and seek to estimate a joint parameter that best explains a set of observations.
no code implementations • 6 Dec 2016 • Angelia Nedić, Alex Olshevsky, César A. Uribe
We show a convergence rate of $O(1/k)$ with the constant term depending on the number of agents and the topology of the network.
no code implementations • 23 Sep 2016 • Angelia Nedić, Alex Olshevsky, César A. Uribe
We overview some results on distributed learning with focus on a family of recently proposed algorithms known as non-Bayesian social learning.
no code implementations • 19 Sep 2016 • Angelia Nedić, Alex Olshevsky, Wei Shi, César A. Uribe
A recent algorithmic family for distributed optimization, DIGing's, have been shown to have geometric convergence over time-varying undirected/directed graphs.
no code implementations • 6 May 2016 • Angelia Nedić, Alex Olshevsky, César Uribe
We consider a distributed learning setup where a network of agents sequentially access realizations of a set of random variables with unknown distributions.
no code implementations • 15 Mar 2016 • Farzad Yousefian, Angelia Nedić, Uday V. Shanbha
Moreover, the rate of convergence in terms of the objective function value is derived.
Optimization and Control
no code implementations • 31 Aug 2015 • Soomin Lee, Angelia Nedić, Maxim Raginsky
In ODA-C, to mitigate the disagreements on the primal-vector updates, the agents implement a generalization of the local information-exchange dynamics recently proposed by Li and Marden over a static undirected graph.
no code implementations • 1 Jul 2013 • Maxim Raginsky, Angelia Nedić
We study a model of collective real-time decision-making (or learning) in a social network operating in an uncertain environment, for which no a priori probabilistic model is available.
no code implementations • 8 Mar 2008 • Angelia Nedić, Alex Olshevsky, Asuman Ozdaglar, John N. Tsitsiklis
We consider a convex unconstrained optimization problem that arises in a network of agents whose goal is to cooperatively optimize the sum of the individual agent objective functions through local computations and communications.
Optimization and Control