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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 401–450 of 536 papers

TitleStatusHype
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques—0
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization—0
Solving Non-smooth Constrained Programs with Lower Complexity than O(1/ ): A Primal-Dual Homotopy Smoothing Approach—0
Sparse-SignSGD with Majority Vote for Communication-Efficient Distributed Learning—0
Sparse sketches with small inversion bias—0
Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD—0
Sparsity Constrained Distributed Unmixing of Hyperspectral Data—0
Spatial Reuse in Dense Wireless Areas: A Cross-layer Optimization Approach via ADMM—0
Distributed Optimization by Network Flows with Spatio-Temporal Compression—0
Spatio-Temporal Communication Compression in Distributed Prime-Dual Flows—0
StochaLM: a Stochastic alternate Linearization Method for distributed optimization—0
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network—0
Stochastic, Distributed and Federated Optimization for Machine Learning—0
Stochastic Distributed Optimization for Machine Learning from Decentralized Features—0
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis—0
Straggler Mitigation in Distributed Optimization Through Data Encoding—0
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers—0
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction—0
Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization—0
SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization—0
Supervised MPC control of large-scale electricity networks via clustering methods—0
Survey of Distributed Algorithms for Resource Allocation over Multi-Agent Systems—0
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data—0
The Communication Complexity of Optimization—0
The Geometry of Sign Gradient Descent—0
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication—0
The Minimax Complexity of Distributed Optimization—0
Tie-Line Characteristics based Partitioning for Distributed Optimization of Power Systems—0
Tighter Performance Theory of FedExProx—0
Toward Communication Efficient Adaptive Gradient Method—0
Towards privacy-preserving cooperative control via encrypted distributed optimization—0
Towards Scalable Multi-View Reconstruction of Geometry and Materials—0
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent—0
Training Deep Neural Networks via Optimization Over Graphs—0
Trajectory Normalized Gradients for Distributed Optimization—0
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs—0
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?—0
Uncertain Multi-Agent Systems with Distributed Constrained Optimization Missions and Event-Triggered Communications: Application to Resource Allocation—0
Understanding A Class of Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective—0
Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning—0
Variance Reduction in Deep Learning: More Momentum is All You Need—0
vqSGD: Vector Quantized Stochastic Gradient Descent—0
When Evolutionary Computation Meets Privacy—0
Widely-distributed Radar Imaging Based on Consensus ADMM—0
Without-Replacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization—0
Without-Replacement Sampling for Stochastic Gradient Methods—0
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
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