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Found 24 papers, 2 papers with code
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation
This paper proposes two nonlinear dynamics to solve constrained distributed optimization problem for resource allocation over a multi-agent network.
Consensus-based Networked Tracking in Presence of Heterogeneous Time-Delays
The tracking part is based on linear time-difference-of-arrival (TDOA) measurement proposed in our previous works.
Distributed Constraint-Coupled Optimization over Lossy Networks
This paper considers distributed resource allocation and sum-preserving constrained optimization over lossy networks, where the links are unreliable and subject to packet drops.
Distributed saddle point problems for strongly concave-convex functions
In this paper, we propose GT-GDA, a distributed optimization method to solve saddle point problems of the form: $\min_{\mathbf{x}} \max_{\mathbf{y}} \{F(\mathbf{x},\mathbf{y}) :=G(\mathbf{x}) + \langle \mathbf{y}, \overline{P} \mathbf{x} \rangle - H(\mathbf{y})\}$, where the functions $G(\cdot)$, $H(\cdot)$, and the the coupling matrix $\overline{P}$ are distributed over a strongly connected network of nodes.
Variance reduced stochastic optimization over directed graphs with row and column stochastic weights
This paper proposes AB-SAGA, a first-order distributed stochastic optimization method to minimize a finite-sum of smooth and strongly convex functions distributed over an arbitrary directed graph.
Distributed Detection and Mitigation of Biasing Attacks over Multi-Agent Networks
First, for performance analysis in the attack-free case, we show that the proposed distributed estimation is unbiased with bounded mean-square deviation in steady-state.
Simultaneous Distributed Estimation and Attack Detection/Isolation in Social Networks: Structural Observability, Kronecker-Product Network, and Chi-Square Detector
This paper considers distributed estimation of linear systems when the state observations are corrupted with Gaussian noise of unbounded support and under possible random adversarial attacks.
Analysis of Contractions in System Graphs: Application to State Estimation
Observability and estimation are closely tied to the system structure, which can be visualized as a system graph--a graph that captures the inter-dependencies within the state variables.
Distributed support-vector-machine over dynamic balanced directed networks
In this paper, we consider the binary classification problem via distributed Support-Vector-Machines (SVM), where the idea is to train a network of agents, with limited share of data, to cooperatively learn the SVM classifier for the global database.
Consensus-Based Distributed Estimation in the Presence of Heterogeneous, Time-Invariant Delays
Classical distributed estimation scenarios typically assume timely and reliable exchanges of information over the sensor network.
A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization
This paper considers decentralized stochastic optimization over a network of $n$ nodes, where each node possesses a smooth non-convex local cost function and the goal of the networked nodes is to find an $\epsilon$-accurate first-order stationary point of the sum of the local costs.
Fast-Convergent Dynamics for Distributed Allocation of Resources Over Switching Sparse Networks with Quantized Communication Links
The idea is to optimally allocate the resources among the group of agents by minimizing the overall cost function subject to fixed sum of resources.
A fast randomized incremental gradient method for decentralized non-convex optimization
For general smooth non-convex problems, we show the almost sure and mean-squared convergence of GT-SAGA to a first-order stationary point and further describe regimes of practical significance where it outperforms the existing approaches and achieves a network topology-independent iteration complexity respectively.
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.
Fast decentralized non-convex finite-sum optimization with recursive variance reduction
We show that GT-SARAH, with appropriate algorithmic parameters, finds an $\epsilon$-accurate first-order stationary point with $O\big(\max\big\{N^{\frac{1}{2}}, n(1-\lambda)^{-2}, n^{\frac{2}{3}}m^{\frac{1}{3}}(1-\lambda)^{-1}\big\}L\epsilon^{-2}\big)$ gradient complexity, where ${(1-\lambda)\in(0, 1]}$ is the spectral gap of the network weight matrix and $L$ is the smoothness parameter of the cost functions.
Push-SAGA: A decentralized stochastic algorithm with variance reduction over directed graphs
In this paper, we propose Push-SAGA, a decentralized stochastic first-order method for finite-sum minimization over a directed network of nodes.
An improved convergence analysis for decentralized online stochastic non-convex optimization
In this paper, we study decentralized online stochastic non-convex optimization over a network of nodes.
S-ADDOPT: Decentralized stochastic first-order optimization over directed graphs
In this report, we study decentralized stochastic optimization to minimize a sum of smooth and strongly convex cost functions when the functions are distributed over a directed network of nodes.
Gradient tracking and variance reduction for decentralized optimization and machine learning
Decentralized methods to solve finite-sum minimization problems are important in many signal processing and machine learning tasks where the data is distributed over a network of nodes and raw data sharing is not permitted due to privacy and/or resource constraints.
Variance-Reduced Decentralized Stochastic Optimization with Gradient Tracking -- Part II: GT-SVRG
Decentralized stochastic optimization has recently benefited from gradient tracking methods \cite{DSGT_Pu, DSGT_Xin} providing efficient solutions for large-scale empirical risk minimization problems.
Variance-Reduced Decentralized Stochastic Optimization with Gradient Tracking
In this paper, we study decentralized empirical risk minimization problems, where the goal to minimize a finite-sum of smooth and strongly-convex functions available over a network of nodes.
Optimization and Control
An introduction to decentralized stochastic optimization with gradient tracking
Decentralized solutions to finite-sum minimization are of significant importance in many signal processing, control, and machine learning applications.
Distributed stochastic optimization with gradient tracking over strongly-connected networks
In this paper, we study distributed stochastic optimization to minimize a sum of smooth and strongly-convex local cost functions over a network of agents, communicating over a strongly-connected graph.
Distributed Nesterov gradient methods over arbitrary graphs
In this letter, we introduce a distributed Nesterov method, termed as $\mathcal{ABN}$, that does not require doubly-stochastic weight matrices.
