Author

Jun-Yu Zhang

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#machine learning Dec 2025

Non-Asymptotic Global Convergence of PPO-Clip

The theoretical foundations of the PPO-Clip algorithm are advanced by analyzing a deterministic actor-only PPO algorithm within the general RL setting with \(f\)-divergence regularization under the softmax policy parameterization and derive a non-uniform Lipschitz smoothness condition and aojasiewicz inequality for the...

Yin Liu, Qi Dai, Jun-Yu Zhang et al. · arXiv.org · 2 citations

Preprint Aug 2026

Stochastic Saddle Avoidance Beyond Unit Excitation and Smoothness: A Pathwise Lyapunov-Perron Framework

Unit excitation (UE) is a common assumption in stochastic saddle avoidance: the stochastic error must have a uniformly positive component along every direction, in expectation. This condition gives a direct way to rule out convergence to strict saddles, but it also oversimplifies the actual noise structure, and does no...

Jun-Wen Qiu, Bohao Ma, Andre Milzarek et al. · 0 citations

Review Open access Aug 2026

The Augmented Lagrangian Methods: Overview and Recent Advances

A unified and comprehensive perspective on constructing augmented Lagrangian functions (based on the Hestenes–Powell–Rockafellar augmented Lagrangian) for various optimization problems, including nonlinear programming and convex and nonconvex composite programming.

Kangkang Deng, Rui Wang, Zhen-Yuan Zhu et al. · SIAM Review · 0 citations