3 papers
math.OC2026
Stochastic Auto-conditioned Fast Gradient Methods with Optimal Rates
Yao Ji, Guanghui Lan
Achieving optimal rates for stochastic composite convex optimization without prior knowledge of problem parameters remains a central challenge. In the deterministic setting, the au…
math.OC2025
Global Solutions to Non-Convex Functional Constrained Problems with Hidden Convexity
Ilyas Fatkhullin, Niao He, Guanghui Lan +1
Constrained non-convex optimization is fundamentally challenging, as global solutions are generally intractable and constraint qualifications may not hold. However, in many applica…
math.OC2025
Can SGD Handle Heavy-Tailed Noise?
Ilyas Fatkhullin, Florian Hübler, Guanghui Lan
Stochastic Gradient Descent (SGD) is a cornerstone of large-scale optimization, yet its theoretical behavior under heavy-tailed noise -- common in modern machine learning and reinf…