In this paper, we focus on a class of non-convex objective functions satisfying the Polyak–Łojasiewicz (PL) condition and present a unified convergence rate ...

This paper is concerned with convergence of stochastic gradient algorithms with momentum terms in the nonconvex setting. A class of stochastic momentum methods, ...

May 30, 2022 · Last-iterate convergence of the stochastic momentum methods is proven for the first time in the non-convex setting, without bounded weight.

Feb 6, 2022 · The Gradient Descent Algorithm can converge on (deterministic) convex, differentiable and Lipschitz Continuous functions.

Abstract—Stochastic gradient descent (SGD) is a popular and efficient method with wide applications in training deep neural nets and other nonconvex models.

Missing: momentum | Show results with:momentum

We present a unified convergence analysis of the gra- dient's norm of the training objective of these stochas- tic methods for non-convex optimization, ...

Stochastic gradient methods with momentum are widely used in applications and at the core of op- timization subroutines in many popular machine.

Stochastic gradient descent (SGD) is a popular and efficient method with wide applications in training deep neural nets and other nonconvex models.

Jun 10, 2024 · Stochastic momentum methods for non-convex learning without bounded assumptions. Abstract. Stochastic momentum methods are widely used to ...