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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 126–150 of 536 papers

TitleStatusHype
Zeroth-Order Feedback-Based Optimization for Distributed Demand Response—0
Correlation Aware Sparsified Mean Estimation Using Random Projection—0
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation—0
Machine Learning Infused Distributed Optimization for Coordinating Virtual Power Plant Assets—0
Distributed Linear Regression with Compositional Covariates—0
Distributed Continuous-Time Optimization with Uncertain Time-Varying Quadratic Cost Functions—0
Detecting Shared Data Manipulation in Distributed Optimization Algorithms—0
LASER: Linear Compression in Wireless Distributed Optimization—0
Network-aware EV charging and discharging in unbalanced distribution grids: A distributed, robust approach against communication failures—0
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved RatesCode0
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization—0
Transmission Investment Coordination using MILP Lagrange Dual Decomposition and Auxiliary Problem PrincipleCode0
Federated Multi-Level Optimization over Decentralized Networks—0
Decentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization—0
Federated Conditional Stochastic Optimization—0
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise—0
A primal-dual perspective for distributed TD-learningCode0
Privacy-Preserving Distributed Market Mechanism for Active Distribution Networks—0
On Linear Convergence of PI Consensus Algorithm under the Restricted Secant Inequality—0
CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity—0
Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM—0
Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data—0
Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication—0
Moreau Envelope ADMM for Decentralized Weakly Convex Optimization—0
Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition—0
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