{
  "id": 18075448,
  "title": "A simple adaptive accelerated optimistic dual averaging method for unconstrained convex optimization in $p$-norm geometry",
  "url": "https://urgent.news/2026/11/03/a-simple-adaptive-accelerated-optimistic-dual-averaging-method-for",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-03T18:35:20.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.05334v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Efficient preconditioning plays an important role in large-scale first-order optimization. In this paper, we propose an adaptive accelerated optimistic dual averaging ($\\text{A}^2$ODA) method for unconstrained convex optimization that adopts low-cost preconditioning based on $p$-norm geometry for $p\\in[2,\\infty]$. The method is line-search-free and employs a new stepsize selection technique based…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}