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math.OC2026
Computation of Strong Solutions to Stochastic Variational Inequalities
Yao Ji, Guanghui Lan, Jason Zhu
This paper studies the computation of strong solutions of monotone variational inequalities (VIs) with Lipschitz continuous operators. Building on the idea of accumulative regulari…
math.OC2026
Stochastic Auto-conditioned Fast Gradient Methods with Optimal Rates
Yao Ji, Guanghui Lan
Achieving optimal rates for stochastic composite convex optimization without prior knowledge of problem parameters remains a central challenge. In the deterministic setting, the au…
math.OC2025
High-order Accumulative Regularization for Gradient Minimization in Convex Programming
Yao Ji, Guanghui Lan
This paper develops a unified high-order accumulative regularization (AR) framework for convex and uniformly convex gradient norm minimization. Existing high-order methods often ex…