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Distributed Optimization

The goal of Distributed Optimization is to optimize a certain objective defined over millions of billions of data that is distributed over many machines by utilizing the computational power of these machines.

Source: Analysis of Distributed StochasticDual Coordinate Ascent

Papers

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Showing 451–500 of 536 papers

TitleStatusHype
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates—0
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization—0
A Distributed Second-Order Algorithm You Can Trust—0
Distributed learning with compressed gradients—0
Distributed Optimization Strategy for Multi Area Economic Dispatch Based on Electro Search Optimization Algorithm—0
Double Quantization for Communication-Efficient Distributed Optimization—0
Tie-Line Characteristics based Partitioning for Distributed Optimization of Power Systems—0
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients—0
Communication-Efficient Projection-Free Algorithm for Distributed Optimization—0
Model Aggregation via Good-Enough Model Spaces—0
GoSGD: Distributed Optimization for Deep Learning with Gossip Exchange—0
Fundamental Resource Trade-offs for Encoded Distributed Optimization—0
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations—0
SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization—0
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning—0
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth RegularizationCode0
Convergence rate of sign stochastic gradient descent for non-convex functions—0
ZOOpt: Toolbox for Derivative-Free OptimizationCode0
Optimal Algorithms for Distributed Optimization—0
Accelerated consensus via Min-Sum Splitting—0
CSWA: Aggregation-Free Spatial-Temporal Community Sensing—0
Straggler Mitigation in Distributed Optimization Through Data Encoding—0
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine LearningCode0
Distributed Unmixing of Hyperspectral Data With Sparsity Constraint—0
Zeroth Order Nonconvex Multi-Agent Optimization over Networks—0
Gradient Sparsification for Communication-Efficient Distributed Optimization—0
A Sequential Approximation Framework for Coded Distributed Optimization—0
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization—0
Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus—0
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization—0
Debiased distributed learning for sparse partial linear models in high dimensions—0
Stochastic, Distributed and Federated Optimization for Machine Learning—0
Adaptive Consensus ADMM for Distributed Optimization—0
Distributed Algorithms for Feature Extraction Off-loading in Multi-Camera Visual Sensor Networks—0
Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity—0
DOPE: Distributed Optimization for Pairwise Energies—0
Private Learning on Networks: Part II—0
Optimal algorithms for smooth and strongly convex distributed optimization in networksCode0
An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise—0
Hemingway: Modeling Distributed Optimization Algorithms—0
Training Deep Neural Networks via Optimization Over Graphs—0
An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids—0
Private Learning on Networks—0
Distributed Multi-Task Relationship Learning—0
Without-Replacement Sampling for Stochastic Gradient Methods—0
A primal-dual method for conic constrained distributed optimization problems—0
CoCoA: A General Framework for Communication-Efficient Distributed OptimizationCode0
Optimization for Large-Scale Machine Learning with Distributed Features and ObservationsCode0
Federated Optimization: Distributed Machine Learning for On-Device Intelligence—0
Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes—0
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