Publications associées à Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

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Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Nicolas Henri Bernard Flammarion, Hristo Georgiev Papazov, Scott William Pesme

In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

2024

Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

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Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

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Universal and adaptive methods for robust stochastic optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

Data Downloaded via Parachute from a NASA Super-Pressure Balloon