Stochastic Adaptive Gradient Descent Without Descent

• URL: http://arxiv.org/abs/2509.14969v1
• Date: Thu, 18 Sep 2025 14:02:10 GMT
• Title: Stochastic Adaptive Gradient Descent Without Descent
• Authors: Jean-François Aujol, Jérémie Bigot, Camille Castera,
• Abstract summary: We introduce a new adaptive step-size strategy for convex optimization with gradient that exploits the local geometry of the objective function only by means of a first-order oracle.<n>We prove the convergence of gradient descent with our stepsize under various assumptions and show that it competes against tuned baselines.
• Score: 1.9499120576896232
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a new adaptive step-size strategy for convex optimization with stochastic gradient that exploits the local geometry of the objective function only by means of a first-order stochastic oracle and without any hyper-parameter tuning. The method comes from a theoretically-grounded adaptation of the Adaptive Gradient Descent Without Descent method to the stochastic setting. We prove the convergence of stochastic gradient descent with our step-size under various assumptions, and we show that it empirically competes against tuned baselines.

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