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Found 8 papers, 0 papers with code
Linear Convergence of Pre-Conditioned PI Consensus Algorithm under Restricted Strong Convexity
This paper considers solving distributed convex optimization problems in peer-to-peer multi-agent networks.
A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning
We prove the convergence of the proposed AdamSSM algorithm.
On Accelerating Distributed Convex Optimizations
The system comprises multiple agents in this problem, each with a set of local data points and an associated local cost function.
Generalized AdaGrad (G-AdaGrad) and Adam: A State-Space Perspective
Two of the prominent accelerated gradient algorithms are AdaGrad and Adam.
Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem
This paper considers the problem of multi-agent distributed linear regression in the presence of system noises.
Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning
The recently proposed Iteratively Pre-conditioned Gradient-descent (IPG) method has been shown to converge faster than other existing distributed algorithms that solve this problem.
Iterative Pre-Conditioning for Expediting the Gradient-Descent Method: The Distributed Linear Least-Squares Problem
In this problem, the system comprises multiple agents, each having a set of local data points, that are connected to a server.
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method
In this problem, there are multiple agents in the system, and each agent only knows its local cost function.
