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Gonglin Yuan on Muck Rack

Gonglin Yuan

As seen in: Nature, arXiv, MDPI
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Articles

Stochastic three-term conjugate gradient: a third-order curvature approximation correction algorithmic framework for machine learning

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By Jiazhen Liu, Gonglin Yuan, Zhongyu Mo
| Nature Verified
Abstract Classical conjugate gradient methods rely solely on first-order information, which limits their ability to capture curvature information in nonconvex stochastic optimization. To address this limitation, this paper proposes a stochastic three-term conjugate gradient algorithm incorporating third-order curvature approximation (TASCG).
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[2409.05002] A Diagonal BFGS Update Algorithm with Inertia Acceleration Technology for Minimizations

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By Gonglin Yuan, Hongtruong Pham
| arXiv
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Some convergently three-term trust region conjugate gradient algorithms under gradient function non-Lipschitz continuity - Scientific Reports

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By Gonglin Yuan
| Nature Verified
Abstract This paper introduces two three-term trust region conjugate gradient algorithms, TT-TR-WP and TT-TR-CG, which are capable of converging under non-Lipschitz continuous gradient functions without any additional conditions. These algorithms possess sufficient descent and trust region properties, and demonstrate global convergence.
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