AINewsnow

Proximal Methods: Why Splitting a Nonsmooth Problem Beats Solving It Directly

Many problems in machine learning look deceptively simple: minimise a smooth loss plus a penalty that encourages structure. The loss is differentiable, the penalty is not, and that one nonsmooth term breaks gradient descent. Proximal methods are the standard answer, and they work for a reason that…

Read the full story at DEV Community — Machine Learning ↗

Timeline · 1 report

  1. 2026-11-01 07:57 · DEV Community — Machine Learning
    Proximal Methods: Why Splitting a Nonsmooth Problem Beats Solving It Directly

More stories

  1. Anthropic files paperwork for IPO as Blackstone flags junk-grade rating — New York Times Technology
  2. DeepSeek open-sources Super-agent, a proactive autonomous coding agent prototype — Techmeme
  3. Mistral releases weights for trillion-parameter Le Chonk model with 1M-token context — Wired AI
  4. Brazil courts data centers with tax breaks and cheap power as US opposition grows — Fortune AI
  5. Nace.AI Open-Sources Drex-Max v1, a 27B Multimodal Decision Model for Local Use — r/LocalLLM
  6. Big Tech sheds nearly $900bn as AI spending fears trigger market rout — Financial Times AI
  7. Beginner lesson plan offers 15-minute introduction to using ChatGPT — Tom's Guide AI
  8. Foxconn hints at new OpenAI tie-up for AI hardware, says no AI bubble — Economic Times Tech

Get the daily brief of stories like this at 6:30 every morning →