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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 101–125 of 536 papers

TitleStatusHype
Combining Graph Attention Networks and Distributed Optimization for Multi-Robot Mixed-Integer Convex Programming—0
Approximate Gradient Coding with Optimal Decoding—0
A primal-dual method for conic constrained distributed optimization problems—0
Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization—0
Communication-Efficient Accurate Statistical Estimation—0
Communication Efficient, Differentially Private Distributed Optimization using Correlation-Aware Sketching—0
Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss—0
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems—0
Information-Geometric Barycenters for Bayesian Federated Learning—0
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow—0
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—0
Communication-Efficient Distributed Kalman Filtering using ADMM—0
A Sequential Approximation Framework for Coded Distributed Optimization—0
Communication-Efficient Distributed SGD with Compressed Sensing—0
Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting—0
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data—0
Communication-Efficient Projection-Free Algorithm for Distributed Optimization—0
Communication-efficient Variance-reduced Stochastic Gradient Descent—0
Adaptive Consensus ADMM for Distributed Optimization—0
Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization—0
Consensus optimization approach for distributed Kalman filtering: performance recovery of centralized filtering with proofs—0
Continual Learning with Distributed Optimization: Does CoCoA Forget?—0
Convergence rate of sign stochastic gradient descent for non-convex functions—0
Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems—0
Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions—0
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