Publications associées à Asynchronous updates for stochastic gradient descent

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Convergence of adaptive algorithms for constrained weakly convex optimization

Volkan Cevher, Ahmet Alacaoglu, Yurii Malitskyi

We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the

\tilde O(t^{-1/2})

rate of convergence for the squared norm of the gradient of Moreau envelope, ...

2021

Asynchronous updates for stochastic gradient descent

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Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

Convergence of adaptive algorithms for constrained weakly convex optimization

Convergence without Convexity: Sampling, Optimization, and Games

Second-order guarantees in centralized, federated and decentralized nonconvex optimization

A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

On the Influence of Bias-Correction on Distributed Stochastic Optimization

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

A Catalyst Framework for Minimax Optimization

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

Convergence of adaptive algorithms for constrained weakly convex optimization

Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

Convergence without Convexity: Sampling, Optimization, and Games

A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain

Second-order guarantees in centralized, federated and decentralized nonconvex optimization

On the Influence of Bias-Correction on Distributed Stochastic Optimization

A Catalyst Framework for Minimax Optimization

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms