1 code implementation • 15 Feb 2024 • Ali AhmadiTeshnizi, Wenzhi Gao, Madeleine Udell
Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare.
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 • 25 Jan 2024 • Wenzhi Gao, Qi Deng
This paper considers stochastic weakly convex optimization without the standard Lipschitz continuity assumption.
1 code implementation • 9 Oct 2023 • Ali AhmadiTeshnizi, Wenzhi Gao, Madeleine Udell
Optimization problems are pervasive across various sectors, from manufacturing and distribution to healthcare.
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).
1 code implementation • 2 Sep 2022 • Zhaonan Qu, Wenzhi Gao, Oliver Hinder, Yinyu Ye, Zhengyuan Zhou
Moreover, our implementation of customized solvers, combined with a random row/column sampling step, can find near-optimal diagonal preconditioners for matrices up to size 200, 000 in reasonable time, demonstrating their practical appeal.
no code implementations • NeurIPS 2021 • Qi Deng, Wenzhi Gao
Second, motivated by the success of momentum stochastic gradient descent, we propose a new stochastic extrapolated model-based method, greatly extending the classic Polyak momentum technique to a wider class of stochastic algorithms for weakly convex optimization.