Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Distributed Optimization Strategy for Multi Area Economic Dispatch Based on Electro Search Optimization Algorithm
A new adopted evolutionary algorithm is presented in this paper to solve the non-smooth, non-convex and non-linear multi-area economic dispatch (MAED). MAED includes some areas which contains its own power generation and…
Distributed OptimizationTie-Line Characteristics based Partitioning for Distributed Optimization of Power Systems
The convergence performance of distributed optimization algorithms is of significant importance to solve optimal power flow (OPF) in a distributed fashion. In this paper, we aim to provide some insights on how to partiti…
ClusteringDistributed OptimizationGraph ClusteringScalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients
In this paper, we explore using deep reinforcement learning for problems with multiple agents. Most existing methods for deep multi-agent reinforcement learning consider only a small number of agents. When the number of …
Deep Reinforcement LearningDistributed OptimizationMulti-agent Reinforcement Learningreinforcement-learning+2Communication-Efficient Projection-Free Algorithm for Distributed Optimization
Distributed optimization has gained a surge of interest in recent years. In this paper we propose a distributed projection free algorithm named Distributed Conditional Gradient Sliding(DCGS). Compared to the state-of-the…
Distributed OptimizationMatrix CompletionModel Aggregation via Good-Enough Model Spaces
In many applications, the training data for a machine learning task is partitioned across multiple nodes, and aggregating this data may be infeasible due to communication, privacy, or storage constraints. Existing distri…
Distributed OptimizationmodelSentiment AnalysisGoSGD: Distributed Optimization for Deep Learning with Gossip Exchange
We address the issue of speeding up the training of convolutional neural networks by studying a distributed method adapted to stochastic gradient descent. Our parallel optimization setup uses several threads, each applyi…
Deep LearningDistributed OptimizationFundamental Resource Trade-offs for Encoded Distributed Optimization
Dealing with the shear size and complexity of today's massive data sets requires computational platforms that can analyze data in a parallelized and distributed fashion. A major bottleneck that arises in such modern dist…
Distributed ComputingDistributed OptimizationA Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations
As the size of modern data sets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pilla…
BIG-bench Machine LearningDistributed ComputingDistributed OptimizationSUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization
We propose and analyze a new stochastic gradient method, which we call Stochastic Unbiased Curvature-aided Gradient (SUCAG), for finite sum optimization problems. SUCAG constitutes an unbiased total gradient tracking tec…
Distributed OptimizationRedundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning
Performance of distributed optimization and learning systems is bottlenecked by "straggler" nodes and slow communication links, which significantly delay computation. We propose a distributed optimization framework where…
Distributed OptimizationregressionA Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization
We propose a communication- and computation-efficient distributed optimization algorithm using second-order information for solving ERM problems with a nonsmooth regularization term. Current second-order and quasi-Newton…
Distributed OptimizationConvergence rate of sign stochastic gradient descent for non-convex functions
The sign stochastic gradient descent method (signSGD) utilizes only the sign of the stochastic gradient in its updates. Since signSGD carries out one-bit quantization of the gradients, it is extremely practical for distr…
Distributed OptimizationQuantizationZOOpt: Toolbox for Derivative-Free Optimization
Recent advances in derivative-free optimization allow efficient approximation of the global-optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous fu…
BIG-bench Machine LearningDistributed OptimizationOptimal Algorithms for Distributed Optimization
In this paper, we study the optimal convergence rate for distributed convex optimization problems in networks. We model the communication restrictions imposed by the network as a set of affine constraints and provide opt…
Distributed OptimizationAccelerated consensus via Min-Sum Splitting
We apply the Min-Sum message-passing protocol to solve the consensus problem in distributed optimization. We show that while the ordinary Min-Sum algorithm does not converge, a modified version of it known as Splitting y…
Distributed OptimizationCSWA: Aggregation-Free Spatial-Temporal Community Sensing
In this paper, we present a novel community sensing paradigm -- {C}ommunity {S}ensing {W}ithout {A}ggregation}. CSWA is designed to obtain the environment information (e.g., air pollution or temperature) in each subarea …
Compressive SensingDistributed OptimizationStraggler Mitigation in Distributed Optimization Through Data Encoding
Slow running or straggler tasks can significantly reduce computation speed in distributed computation. Recently, coding-theory-inspired approaches have been applied to mitigate the effect of straggling, through embedding…
Distributed OptimizationAn Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning
Distributed optimization algorithms are essential for training machine learning models on very large-scale datasets. However, they often suffer from communication bottlenecks. Confronting this issue, a communication-effi…
BIG-bench Machine LearningDistributed OptimizationDistributed Unmixing of Hyperspectral Data With Sparsity Constraint
Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature an…
Distributed OptimizationZeroth Order Nonconvex Multi-Agent Optimization over Networks
In this paper, we consider distributed optimization problems over a multi-agent network, where each agent can only partially evaluate the objective function, and it is allowed to exchange messages with its immediate neig…
Distributed Optimization