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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 151–175 of 536 papers

TitleStatusHype
DEED: A General Quantization Scheme for Communication Efficiency in Bits—0
Deep Distributed Optimization for Large-Scale Quadratic Programming—0
A KL-based Analysis Framework with Applications to Non-Descent Optimization Methods—0
Cost-efficient SVRG with Arbitrary Sampling—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications—0
Design of heterogeneous multi-agent system for distributed computation—0
Detecting Shared Data Manipulation in Distributed Optimization Algorithms—0
Differentially Private Consensus-Based Distributed Optimization—0
Differentially Private Distributed Convex Optimization—0
Correlation Aware Sparsified Mean Estimation Using Random Projection—0
Differentially-Private Distributed Model Predictive Control of Linear Discrete-Time Systems with Global Constraints—0
Asynchronous Forward Bounding for Distributed COPs—0
DiLoCo: Distributed Low-Communication Training of Language Models—0
Discretized Distributed Optimization over Dynamic Digraphs—0
Distributed Continuous-Time Optimization with Uncertain Time-Varying Quadratic Cost Functions—0
Correlated Quantization for Faster Nonconvex Distributed Optimization—0
Distributed Algorithms for Feature Extraction Off-loading in Multi-Camera Visual Sensor Networks—0
Distributed and time-varying primal-dual dynamics via contraction analysis—0
Distributed Asynchronous Dual Free Stochastic Dual Coordinate Ascent—0
Distributed Averaging Methods for Randomized Second Order Optimization—0
Distributed Collision-Free Motion Coordination on a Sphere: A Conic Control Barrier Function Approach—0
Distributed Convex Optimization With Limited Communications—0
Distributed Convex Optimization with State-Dependent (Social) Interactions over Random Networks—0
Correlated quantization for distributed mean estimation and optimization—0
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