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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 176–200 of 536 papers

TitleStatusHype
Distributed CPU Scheduling Subject to Nonlinear Constraints—0
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets—0
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation—0
Distributed Difference of Convex Optimization—0
Distributed Dual Quaternion Based Localization of Visual Sensor Networks—0
Accelerated Distributed Optimization with Compression and Error Feedback—0
Distributed Dynamic Safe Screening Algorithms for Sparse Regularization—0
Distributed Energy Trading Management for Renewable Prosumers with HVAC and Energy Storage—0
Distributed estimation of the inverse Hessian by determinantal averaging—0
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes—0
Distributed Fractional Bayesian Learning for Adaptive Optimization—0
Distributed gradient-based optimization in the presence of dependent aperiodic communication—0
Distributed gradient methods under heavy-tailed communication noise—0
Distributed Training of Graph Convolutional Networks—0
Distributed Graph Learning with Smooth Data Priors—0
Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing—0
Detecting Shared Data Manipulation in Distributed Optimization Algorithms—0
Distributed Learning of Generalized Linear Causal Networks—0
Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems—0
Distributed learning with compressed gradients—0
Distributed Linear Regression with Compositional Covariates—0
Design of heterogeneous multi-agent system for distributed computation—0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning—0
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
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