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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 161–170 of 536 papers

TitleStatusHype
Accelerated Distributed Optimization with Compression and Error Feedback—0
Detecting Shared Data Manipulation in Distributed Optimization Algorithms—0
Design of heterogeneous multi-agent system for distributed computation—0
DiLoCo: Distributed Low-Communication Training of Language Models—0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning—0
Distributed Continuous-Time Optimization with Uncertain Time-Varying Quadratic Cost Functions—0
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
Distributed and time-varying primal-dual dynamics via contraction analysis—0
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization—0
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