no code implementations • 11 Feb 2024 • Wenzhi Gao, Chunlin Sun, Chenyu Xue, Dongdong Ge, Yinyu Ye
More importantly, for the first time, we show that first-order methods can attain regret $\mathcal{O}(T^{1/3})$ with this new framework.
no code implementations • 21 Aug 2023 • Jiyuan Tan, Chenyu Xue, Chuwen Zhang, Qi Deng, Dongdong Ge, Yinyu Ye
In this paper, we propose the stochastic homogeneous second-order descent method (SHSODM) for stochastic functions enjoying gradient dominance property based on a recently proposed homogenization approach.
no code implementations • 21 May 2023 • Yanguang Chen, Wenzhi Gao, Dongdong Ge, Yinyu Ye
We propose a new method to accelerate online Mixed Integer Optimization with Pre-trained machine learning models (PreMIO).
no code implementations • 28 Jan 2023 • Jinsong Liu, Chenghan Xie, Qi Deng, Dongdong Ge, Yinyu Ye
In this paper, we propose several new stochastic second-order algorithms for policy optimization that only require gradient and Hessian-vector product in each iteration, making them computationally efficient and comparable to policy gradient methods.
3 code implementations • 30 Jul 2022 • Chuwen Zhang, Dongdong Ge, Chang He, Bo Jiang, Yuntian Jiang, Yinyu Ye
In this paper, we propose a Dimension-Reduced Second-Order Method (DRSOM) for convex and nonconvex (unconstrained) optimization.
1 code implementation • 18 Feb 2021 • Dongdong Ge, Chengwenjian Wang, Zikai Xiong, Yinyu Ye
The crossover method, which aims at deriving an optimal extreme point from a suboptimal solution (the output of a starting method such as interior-point methods or first-order methods), is crucial in this process.
Optimization and Control 90C05
no code implementations • 16 Mar 2020 • Yining Wang, Xi Chen, Xiangyu Chang, Dongdong Ge
In this paper, using the problem of demand function prediction in dynamic pricing as the motivating example, we study the problem of constructing accurate confidence intervals for the demand function.
no code implementations • NeurIPS 2019 • Dongdong Ge, Haoyue Wang, Zikai Xiong, Yinyu Ye
Computing the Wasserstein barycenter of a set of probability measures under the optimal transport metric can quickly become prohibitive for traditional second-order algorithms, such as interior-point methods, as the support size of the measures increases.