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Found 20 papers, 0 papers with code
Multi-Agent Resilient Consensus under Intermittent Faulty and Malicious Transmissions (Extended Version)
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.
Characterizing Trust and Resilience in Distributed Consensus for Cyberphysical Systems
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
A general framework for decentralized optimization with first-order methods
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.
On the Sample Complexity and Optimization Landscape for Quadratic Feasibility Problems
We consider the problem of recovering a complex vector $\mathbf{x}\in \mathbb{C}^n$ from $m$ quadratic measurements $\{\langle A_i\mathbf{x}, \mathbf{x}\rangle\}_{i=1}^m$.
A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks
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.
Distributed Stochastic Gradient Tracking Methods
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.
A Distributed Stochastic Gradient Tracking Method
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
Distributed Computation of Wasserstein Barycenters over Networks
We propose a new \cu{class-optimal} algorithm for the distributed computation of Wasserstein Barycenters over networks.
Optimal Algorithms for Distributed Optimization
In this paper, we study the optimal convergence rate for distributed convex optimization problems in networks.
On stochastic and deterministic quasi-Newton methods for non-Strongly convex optimization: Asymptotic convergence and rate analysis
To the best of our knowledge, no rate statements currently exist for SQN methods in the absence of such an assumption.
Optimization and Control
Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization
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
Distributed Learning for Cooperative Inference
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.
Distributed Gaussian Learning over Time-varying Directed Graphs
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.
A Tutorial on Distributed (Non-Bayesian) Learning: Problem, Algorithms and Results
We overview some results on distributed learning with focus on a family of recently proposed algorithms known as non-Bayesian social learning.
Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes
A recent algorithmic family for distributed optimization, DIGing's, have been shown to have geometric convergence over time-varying undirected/directed graphs.
Distributed Learning with Infinitely Many Hypotheses
We consider a distributed learning setup where a network of agents sequentially access realizations of a set of random variables with unknown distributions.
Stochastic quasi-Newton methods for non-strongly convex problems: convergence and rate analysis
Moreover, the rate of convergence in terms of the objective function value is derived.
Optimization and Control
Coordinate Dual Averaging for Decentralized Online Optimization with Nonseparable Global Objectives
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.
Online discrete optimization in social networks in the presence of Knightian uncertainty
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.
Distributed Subgradient Methods and Quantization Effects
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
