Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization
The ProxSkip algorithm for distributed optimization is gaining increasing attention due to its proven benefits in accelerating communication complexity while maintaining robustness against data heterogeneity. However, ex…
Distributed OptimizationFederated LearningStochastic OptimizationFederated Multi-Level Optimization over Decentralized Networks
Multi-level optimization has gained increasing attention in recent years, as it provides a powerful framework for solving complex optimization problems that arise in many fields, such as meta-learning, multi-player games…
Distributed OptimizationMeta-Learningreinforcement-learningReinforcement LearningTransmission Investment Coordination using MILP Lagrange Dual Decomposition and Auxiliary Problem Principle
This paper considers the investment coordination problem for the long term transmission capacity expansion in a situation where there are multiple regional Transmission Planners (TPs), each acting in order to maximize th…
Distributed OptimizationDecentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization
Decentralized federated learning (DFL), inherited from distributed optimization, is an emerging paradigm to leverage the explosively growing data from wireless devices in a fully distributed manner.DFL enables joint trai…
Distributed OptimizationFederated LearningFederated Conditional Stochastic Optimization
Conditional stochastic optimization has found applications in a wide range of machine learning tasks, such as invariant learning, AUPRC maximization, and meta-learning. As the demand for training models with large-scale …
Distributed OptimizationFederated LearningMeta-LearningStochastic OptimizationHigh-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
High-probability analysis of stochastic first-order optimization methods under mild assumptions on the noise has been gaining a lot of attention in recent years. Typically, gradient clipping is one of the key algorithmic…
Distributed OptimizationA primal-dual perspective for distributed TD-learning
The goal of this paper is to investigate distributed temporal difference (TD) learning for a networked multi-agent Markov decision process. The proposed approach is based on distributed optimization algorithms, which can…
Distributed OptimizationOn Linear Convergence of PI Consensus Algorithm under the Restricted Secant Inequality
This paper considers solving distributed optimization problems in peer-to-peer multi-agent networks. The network is synchronous and connected. By using the proportional-integral (PI) control strategy, various algorithms …
Distributed OptimizationPrivacy-Preserving Distributed Market Mechanism for Active Distribution Networks
Amidst the worldwide efforts to decarbonize power networks, Local Electricity Markets (LEMs) in distribution networks are gaining importance due to the increased adoption of renewable energy sources and prosumers. Consid…
Distributed OptimizationPrivacy PreservingCORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity
With distributed machine learning being a prominent technique for large-scale machine learning tasks, communication complexity has become a major bottleneck for speeding up training and scaling up machine numbers. In thi…
Distributed OptimizationLimited Communications Distributed Optimization via Deep Unfolded Distributed ADMM
Distributed optimization is a fundamental framework for collaborative inference and decision making in decentralized multi-agent systems. The operation is modeled as the joint minimization of a shared objective which typ…
Collaborative InferenceDecision MakingDistributed OptimizationLinear Speedup of Incremental Aggregated Gradient Methods on Streaming Data
This paper considers a type of incremental aggregated gradient (IAG) method for large-scale distributed optimization. The IAG method is well suited for the parameter server architecture as the latter can easily aggregate…
Distributed OptimizationDistributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication
In this paper, we study unconstrained distributed optimization strongly convex problems, in which the exchange of information in the network is captured by a directed graph topology over digital channels that have limite…
Distributed OptimizationQuantizationMoreau Envelope ADMM for Decentralized Weakly Convex Optimization
This paper proposes a proximal variant of the alternating direction method of multipliers (ADMM) for distributed optimization. Although the current versions of ADMM algorithm provide promising numerical results in produc…
Distributed OptimizationPrivacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition
Distributed optimization is manifesting great potential in multiple fields, e.g., machine learning, control, and resource allocation. Existing decentralized optimization algorithms require sharing explicit state informat…
Distributed OptimizationPrivacy PreservingLearning (With) Distributed Optimization
This paper provides an overview of the historical progression of distributed optimization techniques, tracing their development from early duality-based methods pioneered by Dantzig, Wolfe, and Benders in the 1960s to th…
Distributed OptimizationContinuous-Time Distributed Dynamic Programming for Networked Multi-Agent Markov Decision Processes
The main goal of this paper is to investigate continuous-time distributed dynamic programming (DP) algorithms for networked multi-agent Markov decision problems (MAMDPs). In our study, we adopt a distributed multi-agent …
Distributed OptimizationFederated K-Means Clustering via Dual Decomposition-based Distributed Optimization
The use of distributed optimization in machine learning can be motivated either by the resulting preservation of privacy or the increase in computational efficiency. On the one hand, training data might be stored across …
ClusteringComputational EfficiencyDistributed OptimizationDifferentially Private Distributed Estimation and Learning
We study distributed estimation and learning problems in a networked environment where agents exchange information to estimate unknown statistical properties of random variables from their privately observed samples. The…
Distributed OptimizationDistributed Random Reshuffling Methods with Improved Convergence
This paper proposes two distributed random reshuffling methods, namely Gradient Tracking with Random Reshuffling (GT-RR) and Exact Diffusion with Random Reshuffling (ED-RR), to solve the distributed optimization problem …
Distributed Optimization