Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach
In this paper, we consider the distributed optimal control problem for linear networked systems. In particular, we are interested in learning distributed optimal controllers using graph recurrent neural networks (GRNNs).…
Distributed OptimizationGraph Neural NetworkSelf-Supervised LearningReducing the Communication of Distributed Model Predictive Control: Autoencoders and Formation Control
Communication remains a key factor limiting the applicability of distributed model predictive control (DMPC) in realistic settings, despite advances in wireless communication. DMPC schemes can require an overwhelming amo…
Distributed OptimizationModel Predictive ControlDynamic Incentive Strategies for Smart EV Charging Stations: An LLM-Driven User Digital Twin Approach
This paper presents an enhanced electric vehicle demand response system based on large language models, aimed at optimizing the application of vehicle-to-grid technology. By leveraging an large language models-driven mul…
Decision MakingDistributed OptimizationManagementCombining Graph Attention Networks and Distributed Optimization for Multi-Robot Mixed-Integer Convex Programming
In this paper, we develop a fast mixed-integer convex programming (MICP) framework for multi-robot navigation by combining graph attention networks and distributed optimization. We formulate a mixed-integer optimization …
Distributed OptimizationGraph AttentionMotion PlanningRobot NavigationALADIN-$β$: A Distributed Optimization Algorithm for Solving MPCC Problems
Mathematical Programs with Complementarity Constraints (MPCC) are critical in various real-world applications but notoriously challenging due to non-smoothness and degeneracy from complementarity constraints. The $\ell_1…
Distributed OptimizationConvergence Theory of Flexible ALADIN for Distributed Optimization
The Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) method is a cutting-edge distributed optimization algorithm known for its superior numerical performance. It relies on each agent transmitting inform…
Distributed OptimizationFederated LearningCollaborative Satisfaction of Long-Term Spatial Constraints in Multi-Agent Systems: A Distributed Optimization Approach (extended version)
This paper addresses the problem of collaboratively satisfying long-term spatial constraints in multi-agent systems. Each agent is subject to spatial constraints, expressed as inequalities, which may depend on the positi…
Distributed OptimizationCommunication Efficient Federated Learning with Linear Convergence on Heterogeneous Data
By letting local clients perform multiple local updates before communicating with a parameter server, modern federated learning algorithms such as FedAvg tackle the communication bottleneck problem in distributed learnin…
Distributed OptimizationFederated LearningByzantine-Resilient Federated Learning via Distributed Optimization
Byzantine attacks present a critical challenge to Federated Learning (FL), where malicious participants can disrupt the training process, degrade model accuracy, and compromise system reliability. Traditional FL framewor…
Distributed OptimizationFederated LearningAccelerated Distributed Optimization with Compression and Error Feedback
Modern machine learning tasks often involve massive datasets and models, necessitating distributed optimization algorithms with reduced communication overhead. Communication compression, where clients transmit compressed…
Distributed OptimizationFrom Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges
Federated Learning (FL) enables collaborative learning without directly sharing individual's raw data. FL can be implemented in either a centralized (server-based) or decentralized (peer-to-peer) manner. In this survey, …
Distributed OptimizationFederated LearningDistributed Pose Graph Optimization using the Splitting Method based on the Alternating Direction Method of Multipliers
Distributed optimization aims to leverage the local computation and communication capabilities of each agent to achieve a desired global objective. This paper addresses the distributed pose graph optimization (PGO) probl…
Distributed OptimizationOpportunistic Routing in Wireless Communications via Learnable State-Augmented Policies
This paper addresses the challenge of packet-based information routing in large-scale wireless communication networks. The problem is framed as a constrained statistical learning task, where each network node operates us…
Distributed OptimizationUnbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs
Distributed methods are essential for handling machine learning pipelines comprising large-scale models and datasets. However, their benefits often come at the cost of increased communication overhead between the central…
Distributed OptimizationLanguage ModelingLanguage ModellingQuantizationSmoothed Normalization for Efficient Distributed Private Optimization
Federated learning enables training machine learning models while preserving the privacy of participants. Surprisingly, there is no differentially private distributed method for smooth, non-convex optimization problems. …
Distributed OptimizationFederated LearningFundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton
A substantial body of work in machine learning (ML) and randomized numerical linear algebra (RandNLA) has exploited various sorts of random sketching methodologies, including random sampling and random projection, with m…
Distributed OptimizationStochastic OptimizationSign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness
In recent years, non-convex optimization problems are more often described by generalized $(L_0, L_1)$-smoothness assumption rather than standard one. Meanwhile, severely corrupted data used in these problems has increas…
Distributed OptimizationEfficient Distributed Optimization under Heavy-Tailed Noise
Distributed optimization has become the default training paradigm in modern machine learning due to the growing scale of models and datasets. To mitigate communication overhead, local updates are often applied before glo…
Distributed OptimizationA Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization
As data sets grow in size and complexity, it is becoming more difficult to pull useful features from them using hand-crafted feature extractors. For this reason, deep learning (DL) frameworks are now widely popular. The …
Distributed OptimizationNavigateSecond-order methodsCommunication-Efficient Distributed Kalman Filtering using ADMM
This paper addresses the problem of optimal linear filtering in a network of local estimators, commonly referred to as distributed Kalman filtering (DKF). The DKF problem is formulated within a distributed optimization f…
Distributed Optimization