arXiv:2606. 29593v1 Announce Type: cross Abstract: In 1937, Stefan Kaczmarz proposed a simple algorithm for solving systems of linear equations.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2509. 14969v2 Announce Type: replace 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.
UCLA Professor Ernest Ryu and GPT-5 solved a key question in optimization theory, showcasing AI’s role in accelerating mathematical discovery.
A step-by-step journey from calculus-based optimization to Stochastic Gradient Descent The post Why Gradient Descent Became Stochastic appeared first on Towards Data Science .
arXiv:2608. 04607v1 Announce Type: cross Abstract: Stochastic gradient descent (SGD) optimization methods are the standard instruments for the training of deep neural networks (DNNs).
arXiv:2601. 16510v3 Announce Type: replace-cross Abstract: Solving massive-scale optimization problems requires scalable first-order methods with low per-iteration cost.
arXiv:2606. 01787v1 Announce Type: new Abstract: A new class of asynchronous adaptive first-order optimization methods is introduced, comprising asynchronous variants of several popular algorithms.