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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 451–475 of 536 papers

TitleStatusHype
Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks—0
Zeroth-Order Feedback-Based Optimization for Distributed Demand Response—0
Zeroth Order Nonconvex Multi-Agent Optimization over Networks—0
Semantics, Representations and Grammars for Deep Learning—0
99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it—0
Accelerated consensus via Min-Sum Splitting—0
Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity—0
Accelerated Distributed Optimization with Compression and Error Feedback—0
Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity—0
Accelerated Sparsified SGD with Error Feedback—0
Accelerating Distributed Optimization: A Primal-Dual Perspective on Local Steps—0
Accelerating variational quantum algorithms with multiple quantum processors—0
Acceleration for Compressed Gradient Descent in Distributed Optimization—0
Acceleration in Distributed Optimization under Similarity—0
Differentially Quantized Gradient Methods—0
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting—0
A continuous-time analysis of distributed stochastic gradient—0
Adaptive Consensus ADMM for Distributed Optimization—0
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets—0
Debiased distributed learning for sparse partial linear models in high dimensions—0
A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition—0
A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events—0
A Distributed Second-Order Algorithm You Can Trust—0
ADMM for Downlink Beamforming in Cell-Free Massive MIMO Systems—0
A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks—0
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