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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
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
Advances in Asynchronous Parallel and Distributed Optimization—0
A Federated Distributionally Robust Support Vector Machine with Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis—0
A Hierarchical Approach for Joint Multi-view Object Pose Estimation and Categorization—0
AIDE: Fast and Communication Efficient Distributed Optimization—0
Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information—0
A KL-based Analysis Framework with Applications to Non-Descent Optimization Methods—0
ALADIN-α -- An open-source MATLAB toolbox for distributed non-convex optimization—0
ALADIN-β: A Distributed Optimization Algorithm for Solving MPCC Problems—0
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—0
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent—0
An Equivalent Circuit Approach to Distributed Optimization—0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing—0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids—0
An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise—0
A Novel Decentralized Algorithm for Coordinating the Optimal Power and Traffic Flows with EVs based on Variable Inner Loop Selection—0
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization—0
A Plug and Play Distributed Secondary Controller for Microgrids with Grid-Forming Inverters—0
Approximate Gradient Coding with Optimal Decoding—0
A primal-dual method for conic constrained distributed optimization problems—0
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems—0
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow—0
A Semi-Distributed Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections—0
A Sequential Approximation Framework for Coded Distributed Optimization—0
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations—0
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