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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 501–536 of 536 papers

TitleStatusHype
Distributed Online Optimization in Dynamic Environments Using Mirror Descent—0
AIDE: Fast and Communication Efficient Distributed Optimization—0
Distributed Optimization of Convex Sum of Non-Convex Functions—0
Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUsCode0
Distributed Optimization for Client-Server Architecture with Negative Gradient Weights—0
Distributed Asynchronous Dual Free Stochastic Dual Coordinate Ascent—0
Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing—0
Without-Replacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization—0
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual FrameworkCode0
Distributed Optimization with Arbitrary Local SolversCode0
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications—0
Federated Optimization:Distributed Optimization Beyond the Datacenter—0
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications to Parallel Machine Learning and Multi-Label Image Segmentation—0
Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization?—0
Semantics, Representations and Grammars for Deep Learning—0
Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis—0
Distributed Stochastic Variance Reduced Gradient Methods and A Lower Bound for Communication Complexity—0
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty—0
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections—0
ELM-Based Distributed Cooperative Learning Over Networks—0
A Hierarchical Approach for Joint Multi-view Object Pose Estimation and Categorization—0
Adding vs. Averaging in Distributed Primal-Dual OptimizationCode0
Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss—0
Online Distributed Optimization on Dynamic Networks—0
Communication-Efficient Distributed Dual Coordinate Ascent—0
High-performance Kernel Machines with Implicit Distributed Optimization and Randomization—0
ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images—0
Asynchronous Forward Bounding for Distributed COPs—0
Communication Efficient Distributed Optimization using an Approximate Newton-type MethodCode0
Asynchronous Adaptation and Learning over Networks - Part II: Performance Analysis—0
Asynchronous Adaptation and Learning over Networks --- Part I: Modeling and Stability Analysis—0
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent—0
Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization—0
Diffusion Adaptation over Networks—0
Distributed Delayed Stochastic Optimization—0
Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling—0
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