Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques
To address the high communication costs of distributed machine learning, a large body of work has been devoted in recent years to designing various compression strategies, such as sparsification and quantization, and opt…
BIG-bench Machine LearningDistributed OptimizationQuantizationAuction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions
We investigate the use of multi-agent allocation techniques on problems related to Earth observation scenarios with multiple users and satellites. We focus on the problem of coordinating users having reserved exclusive o…
Distributed OptimizationEarth ObservationSchedulingPixel super-resolved lensless on-chip sensor with scattering multiplexing
Lensless on-chip microscopy has shown great potential for biomedical imaging due to its large-area and high-throughput imaging capabilities. By combining the pixel super-resolution (PSR) technique, it can improve the res…
Computational EfficiencyDistributed OptimizationDiversitySuper-ResolutionAssessing the Impacts of Nonideal Communications on Distributed Optimal Power Flow Algorithms
Power system operators are increasingly looking toward distributed optimization to address various challenges facing electric power systems. To assess their capabilities in environments with nonideal communications, this…
Distributed OptimizationOptimization in Open Networks via Dual Averaging
In networks of autonomous agents (e.g., fleets of vehicles, scattered sensors), the problem of minimizing the sum of the agents' local functions has received a lot of interest. We tackle here this distributed optimizatio…
Distributed OptimizationLocalNewton: Reducing Communication Bottleneck for Distributed Learning
To address the communication bottleneck problem in distributed optimization within a master-worker framework, we propose LocalNewton, a distributed second-order algorithm with local averaging. In LocalNewton, the worker …
Distributed OptimizationInnovation Compression for Communication-efficient Distributed Optimization with Linear Convergence
Information compression is essential to reduce communication cost in distributed optimization over peer-to-peer networks. This paper proposes a communication-efficient linearly convergent distributed (COLD) algorithm to …
Distributed OptimizationAn Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization
XGBoost is one of the most widely used machine learning models in the industry due to its superior learning accuracy and efficiency. Targeting at data isolation issues in the big data problems, it is crucial to deploy a …
Distributed OptimizationImproving the Transient Times for Distributed Stochastic Gradient Methods
We consider the distributed optimization problem where $n$ agents each possessing a local cost function, collaboratively minimize the average of the $n$ cost functions over a connected network. Assuming stochastic gradie…
Distributed OptimizationDistributed Energy Trading Management for Renewable Prosumers with HVAC and Energy Storage
Heating, ventilating, and air-conditioning (HVAC) systems consume a large amount of energy in residential houses and buildings. Effective energy management of HVAC is a cost-effective way to improve energy efficiency and…
Distributed Optimizationenergy managementenergy tradingManagementMean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing
In this paper, a novel bandwidth negotiation mechanism is proposed for massive devices wireless spectrum sharing, in which individual device locally negotiates bandwidth usage with neighbor devices and globally optimal s…
Decision MakingDistributed OptimizationMulti-agent Reinforcement LearningReinforcement Learning (RL)Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes
This paper focuses on distributed learning-based control of decentralized multi-agent systems where the agents' dynamics are modeled by Gaussian Processes (GPs). Two fundamental problems are considered: the optimal desig…
Computational EfficiencyDistributed OptimizationGaussian ProcessesModel Predictive ControlDistributed Newton-like Algorithms and Learning for Optimized Power Dispatch
This thesis explores a particular class of distributed optimization methods for various separable resource allocation problems, which are of high interest in a wide array of multi-agent settings. A distinctly motivating …
Distributed OptimizationEfficient Randomized Subspace Embeddings for Distributed Optimization under a Communication Budget
We study first-order optimization algorithms under the constraint that the descent direction is quantized using a pre-specified budget of $R$-bits per dimension, where $R \in (0 ,\infty)$. We propose computationally effi…
Distributed OptimizationQuantizationMoshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices
Training deep neural networks on large datasets can often be accelerated by using multiple compute nodes. This approach, known as distributed training, can utilize hundreds of computers via specialized message-passing pr…
Distributed OptimizationFederated LearningGradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games
We are concerned with finding Nash Equilibria in agent-based multi-cluster games, where agents are separated into distinct clusters. While the agents inside each cluster collaborate to achieve a common goal, the clusters…
Distributed OptimizationDistributed Second Order Methods with Fast Rates and Compressed Communication
We develop several new communication-efficient second-order methods for distributed optimization. Our first method, NEWTON-STAR, is a variant of Newton's method from which it inherits its fast local quadratic rate. Howev…
Distributed OptimizationSecond-order methodsSmoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization
Large scale distributed optimization has become the default tool for the training of supervised machine learning models with a large number of parameters and training data. Recent advancements in the field provide severa…
Distributed OptimizationDecentralized Riemannian Gradient Descent on the Stiefel Manifold
We consider a distributed non-convex optimization where a network of agents aims at minimizing a global function over the Stiefel manifold. The global function is represented as a finite sum of smooth local functions, wh…
Distributed OptimizationStraggler-Resilient Distributed Machine Learning with Dynamic Backup Workers
With the increasing demand for large-scale training of machine learning models, consensus-based distributed optimization methods have recently been advocated as alternatives to the popular parameter server framework. In …
BIG-bench Machine LearningDistributed Optimization