Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Asynchronous Distributed Multi-Robot Motion Planning Under Imperfect Communication
This paper addresses the challenge of coordinating multi-robot systems under realistic communication delays using distributed optimization. We focus on consensus ADMM as a scalable framework for generating collision-free…
Distributed OptimizationMotion PlanningLarge Language Model-Assisted Planning of Electric Vehicle Charging Infrastructure with Real-World Case Study
The growing demand for electric vehicle (EV) charging infrastructure presents significant planning challenges, requiring efficient strategies for investment and operation to deliver cost-effective charging services. Howe…
Distributed OptimizationSystemic approach for modeling a generic smart grid
Smart grid technological advances present a recent class of complex interdisciplinary modeling and increasingly difficult simulation problems to solve using traditional computational methods. To simulate a smart grid req…
Distributed OptimizationCommunication-Aware Asynchronous Distributed Trajectory Optimization for UAV Swarm
Distributed optimization offers a promising paradigm for trajectory planning in Unmanned Aerial Vehicle (UAV) swarms, yet its deployment in communication-constrained environments remains challenging due to unreliable lin…
Distributed OptimizationTrajectory PlanningCensus-Based Population Autonomy For Distributed Robotic Teaming
Collaborating teams of robots show promise due in their ability to complete missions more efficiently and with improved robustness, attributes that are particularly useful for systems operating in marine environments. A …
Distributed OptimizationWhy Federated Optimization Fails to Achieve Perfect Fitting? A Theoretical Perspective on Client-Side Optima
Federated optimization is a constrained form of distributed optimization that enables training a global model without directly sharing client data. Although existing algorithms can guarantee convergence in theory and oft…
Distributed OptimizationAspiration-based Perturbed Learning Automata in Games with Noisy Utility Measurements. Part A: Stochastic Stability in Non-zero-Sum Games
Reinforcement-based learning has attracted considerable attention both in modeling human behavior as well as in engineering, for designing measurement- or payoff-based optimization schemes. Such learning schemes exhibit …
Distributed OptimizationARCO-BO: Adaptive Resource-aware COllaborative Bayesian Optimization for Heterogeneous Multi-Agent Design
Modern scientific and engineering design increasingly involves distributed optimization, where agents such as laboratories, simulations, or industrial partners pursue related goals under differing conditions. These agent…
Distributed OptimizationStress-Aware Learning under KL Drift via Trust-Decayed Mirror Descent
We study sequential decision-making under distribution drift. We propose entropy-regularized trust-decay, which injects stress-aware exponential tilting into both belief updates and mirror-descent decisions. On the simpl…
Distributed OptimizationMulti-Robot Distributed Optimization for Exploration and Mapping of Unknown Environments using Bioinspired Tactile-Sensor
This project proposes a bioinspired multi-robot system using Distributed Optimization for efficient exploration and mapping of unknown environments. Each robot explores its environment and creates a map, which is afterwa…
Distributed OptimizationRingleader ASGD: The First Asynchronous SGD with Optimal Time Complexity under Data Heterogeneity
Asynchronous stochastic gradient methods are central to scalable distributed optimization, particularly when devices differ in computational capabilities. Such settings arise naturally in federated learning, where traini…
Distributed OptimizationFederated LearningClapping: Removing Per-sample Storage for Pipeline Parallel Distributed Optimization with Communication Compression
Pipeline-parallel distributed optimization is essential for large-scale machine learning but is challenged by significant communication overhead from transmitting high-dimensional activations and gradients between worker…
Distributed OptimizationSMART: Scalable Multi-Agent Reasoning and Trajectory Planning in Dense Environments
Multi-vehicle trajectory planning is a non-convex problem that becomes increasingly difficult in dense environments due to the rapid growth of collision constraints. Efficient exploration of feasible behaviors and resolu…
Distributed OptimizationReinforcement LearningCollision AvoidanceTrajectory PlanningDecentralized Optimization with Topology-Independent Communication
Distributed optimization requires nodes to coordinate, yet full synchronization scales poorly. When $n$ nodes collaborate through $m$ pairwise regularizers, standard methods demand $\mathcal{O}(m)$ communications per ite…
Distributed OptimizationUDON: Uncertainty-weighted Distributed Optimization for Multi-Robot Neural Implicit Mapping under Extreme Communication Constraints
Multi-robot mapping with neural implicit representations enables the compact reconstruction of complex environments. However, it demands robustness against communication challenges like packet loss and limited bandwidth.…
Distributed OptimizationA Weighted Gradient Tracking Privacy-Preserving Method for Distributed Optimization
This paper investigates the privacy-preserving distributed optimization problem, aiming to protect agents' private information from potential attackers during the optimization process. Gradient tracking, an advanced tech…
Distributed OptimizationFrom PowerSGD to PowerSGD+: Low-Rank Gradient Compression for Distributed Optimization with Convergence Guarantees
Low-rank gradient compression methods, such as PowerSGD, have gained attention in communication-efficient distributed optimization. However, the convergence guarantees of PowerSGD remain unclear, particularly in stochast…
Distributed OptimizationLearning to accelerate distributed ADMM using graph neural networks
Distributed optimization is fundamental to large-scale machine learning and control applications. Among existing methods, the alternating direction method of multipliers (ADMM) has gained popularity due to its strong con…
Distributed OptimizationRobust Real-Time Coordination of CAVs: A Distributed Optimization Framework under Uncertainty
Achieving both safety guarantees and real-time performance in cooperative vehicle coordination remains a fundamental challenge, particularly in dynamic and uncertain environments. Existing methods often suffer from insuf…
Distributed OptimizationComputational EfficiencyCOSMO-Bench: A Benchmark for Collaborative SLAM Optimization
Recent years have seen a focus on research into distributed optimization algorithms for multi-robot Collaborative Simultaneous Localization and Mapping (C-SLAM). Research in this domain, however, is made difficult by a l…
Distributed Optimization