LLM-based agents in Lean solved 9 of 353 open Erdős problems and proved 44 of 492 OEIS conjectures at a few hundred dollars each.
arXiv:2510.23513 (2025)
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Accelerated augmented Lagrangian methods for convex problems achieve convergence and o(1/k^2) rates for feasibility violation and objective residual under suitable parameters.
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An AI model produced a new formula for a central element of U_q(so_12) at the quality level of advanced undergraduate research, along with faster computation via SageMath, prompting changes in mentorship practices.
APAPC integrates Nesterov acceleration into primal-dual forward-backward schemes by exploiting dual strong convexity to achieve optimal sublinear and accelerated linear convergence rates.
The accelerated backward-forward method achieves O(1/k²) convergence on convex composite problems and accelerated linear convergence when the smooth component is strongly convex.
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LLM-based agents in Lean solved 9 of 353 open Erdős problems and proved 44 of 492 OEIS conjectures at a few hundred dollars each.
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