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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 301–310 of 536 papers

TitleStatusHype
Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus—0
DOPE: Distributed Optimization for Pairwise Energies—0
Do Subsampled Newton Methods Work for High-Dimensional Data?—0
Double Quantization for Communication-Efficient Distributed Optimization—0
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization—0
D-SVM over Networked Systems with Non-Ideal Linking Conditions—0
Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling—0
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime—0
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections—0
Dynamic Incentive Strategies for Smart EV Charging Stations: An LLM-Driven User Digital Twin Approach—0
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