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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 191–200 of 536 papers

TitleStatusHype
Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing—0
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications—0
Distributed Learning of Generalized Linear Causal Networks—0
Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems—0
Distributed learning with compressed gradients—0
Distributed Linear Regression with Compositional Covariates—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization—0
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent—0
Deep Learning for Distributed Optimization: Applications to Wireless Resource Management—0
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