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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 476–500 of 536 papers

TitleStatusHype
Gradient Sparsification for Communication-Efficient Distributed Optimization—0
A Sequential Approximation Framework for Coded Distributed Optimization—0
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization—0
Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus—0
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization—0
Debiased distributed learning for sparse partial linear models in high dimensions—0
Stochastic, Distributed and Federated Optimization for Machine Learning—0
Adaptive Consensus ADMM for Distributed Optimization—0
Distributed Algorithms for Feature Extraction Off-loading in Multi-Camera Visual Sensor Networks—0
Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity—0
DOPE: Distributed Optimization for Pairwise Energies—0
Private Learning on Networks: Part II—0
Optimal algorithms for smooth and strongly convex distributed optimization in networksCode0
An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise—0
Hemingway: Modeling Distributed Optimization Algorithms—0
Training Deep Neural Networks via Optimization Over Graphs—0
An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids—0
Private Learning on Networks—0
Distributed Multi-Task Relationship Learning—0
Without-Replacement Sampling for Stochastic Gradient Methods—0
A primal-dual method for conic constrained distributed optimization problems—0
CoCoA: A General Framework for Communication-Efficient Distributed OptimizationCode0
Optimization for Large-Scale Machine Learning with Distributed Features and ObservationsCode0
Federated Optimization: Distributed Machine Learning for On-Device Intelligence—0
Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes—0
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