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math.OC2025
Faster stochastic cubic regularized Newton methods with momentum
Yiming Yang, Chuan He, Xiao Wang +1
Cubic regularized Newton (CRN) methods have attracted signiffcant research interest because they offer stronger solution guarantees and lower iteration complexity. With the rise of…
math.OC2024
A stochastic first-order method with multi-extrapolated momentum for highly smooth unconstrained optimization
Chuan He
In this paper, we consider an unconstrained stochastic optimization problem where the objective function exhibits high-order smoothness. Specifically, we propose a new stochastic f…
math.OC2024
Stochastic interior-point methods for smooth conic optimization with applications
Chuan He, Zhanwang Deng
Conic optimization plays a crucial role in many machine learning (ML) problems. However, practical algorithms for conic constrained ML problems with large datasets are often limite…