Last-Iterate Rate for Anchored Gradient Descent-Ascent

Precise statement

For smooth convex-concave min-max problems, can anchored gradient descent-ascent be scheduled so that its exact last-iterate squared-gradient residual is $O(1/t)$, closing the gap left by the 2019 analysis?

The source statement is reproduced for indexing with attribution. Mathematical correctness requires domain-expert or mechanical review. VibeMath has not independently audited statement fidelity, correctness, priority, or novelty.

What AI did

AlphaProof Nexus

The agent searched for the anchoring schedule and its proof simultaneously, discovering a parameter choice yielding the stronger guarantee via a discrete-time recurrence argument rather than the usual continuous-time ODE analysis.

Provider: Google DeepMind · Prompt public: unknown · Independence: unknown

Verification boundary

lean verified statement audited

Lean-checked; accompanying arXiv preprint by the DeepMind team.

Correctness: supported · statement fidelity: audited · peer review: none

Timeline

1. 2026-04-04

   arXiv:2604.03782 - An improved last-iterate convergence rate for anchored gradient descent ascent

   VibeMathed reports this item as resolved. VibeMath preserves that report as a source assertion and has not independently authored a plain-language mathematical explanation.

Known method families

argument (source-reported)

Source-reported tools: argument.

Independent: unknown · difference confidence: 0

What remains uncertain

VibeMath has not independently audited the mathematical statement, proof, or novelty claim.

• VibeMath has not independently verified the mathematical claim.
• AI-attempt independence and training-data exposure are unknown unless explicitly documented.
• VibeMath has not independently audited the mathematical statement, proof, or novelty claim.