arXiv Artificial Intelligence

SoftServe: A Scalable Quasi-Newton Method for Deep Learning

1 hour ago 02.10.2026 07:00 👁 1 views
SoftServe: A Scalable Quasi-Newton Method for Deep Learning

Quick summary

arXiv:2610.02182v1 Announce Type: cross Abstract: Quasi-Newton (QN) methods have long been among the most effective methods for large-scale unconstrained convex optimization. Two obstacles have limited their use in deep learning: non-convexity and enormous parameter sizes. We introduce SoftServe, a family of QN methods designed to overcome these obstacles without line searches or ad hoc curvature corrections. SoftServe derives positivedefinite curvature estimates from the variational objective of Berglund et al. (2025), even in the presence of negative curvature. We develop diagonal and Kronec

Key takeaways

  • arXiv:2610.02182v1 Announce Type: cross Abstract: Quasi-Newton (QN) methods have long been among the most effective methods for large-scale unconstrained convex optimization.
  • Two obstacles have limited their use in deep learning: non-convexity and enormous parameter sizes.
  • We introduce SoftServe, a family of QN methods designed to overcome these obstacles without line searches or ad hoc curvature corrections.

Why it matters

“SoftServe: A Scalable Quasi-Newton Method for Deep Learning” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗