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Found 5 papers, 1 papers with code
A Variance-Reduced Stochastic Accelerated Primal Dual Algorithm
In this work, we consider strongly convex strongly concave (SCSC) saddle point (SP) problems $\min_{x\in\mathbb{R}^{d_x}}\max_{y\in\mathbb{R}^{d_y}}f(x, y)$ where $f$ is $L$-smooth, $f(., y)$ is $\mu$-strongly convex for every $y$, and $f(x,.
TENGraD: Time-Efficient Natural Gradient Descent with Exact Fisher-Block Inversion
This work proposes a time-efficient Natural Gradient Descent method, called TENGraD, with linear convergence guarantees.
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
We introduce a framework for designing primal methods under the decentralized optimization setting where local functions are smooth and strongly convex.
ASYNC: A Cloud Engine with Asynchrony and History for Distributed Machine Learning
ASYNC is a framework that supports the implementation of asynchrony and history for optimization methods on distributed computing platforms.
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances
In the special case of strongly convex quadratic objectives, we can show accelerated linear rates in the $p$-Wasserstein metric for any $p\geq 1$ with improved sensitivity to noise for both AG and HB through a non-asymptotic analysis under some additional assumptions on the noise structure.
