AI & ML
Convergence rate of stochastic gradient descent under {\L}ojasiewicz type condition and polynomially controlled noise
arXiv cs.NA0 views
arXiv:2610.23386v1 Announce Type: cross Abstract: Stochastic gradient methods are widely used for solving optimization problems in many scientific fields. Recently, their popularity has risen due to the increase in machine learning's prominence. Their convergence is often studied under the convexity assumption, which is too restrictive in modern applications. In this paper, we investigate the almost sure convergence rate and expectation under a {\L}ojasiewicz-type gradient domination condition. This framework includes both bounded and decaying noise, as well as noise with controlled polynomial growth. The obtained results extend existing convergence by allowing a broader class of noise sequences and explicitly capturing the effect of their asymptotic behavior on the convergence rate and applicability.
Source：https://arxiv.org/abs/2610.23386PreviousPINN-LPCDD: Physics-Informed Neural Network with Learnable and Physically Consistent Domain Decomposition for Compressible Multi-Medium FlowsNextCayley access models for matrix functions: reachable class and cost
Related posts
The Download: the agentic web arrives and the drive for safer roads
0MIT Technology Review
Stop counting tokens, start counting what ships
0MIT Technology Review
OpenAI is finally merging ChatGPT’s chat and Work tabs
0TechCrunch
10 Open-Source Robotics Repositories for 3D Perception, VLA Policies, and Sensor Calibration
0dev.to
My self-modifying system tried to wire a gate-passing module into its own protection enforcer. It was refused three times.
0dev.to
Last night an AI agent performed brain surgery on a mouse
0Hacker News