no code implementations • 3 Apr 2024 • Siyi Wang, Zifan Wang, Xinlei Yi, Michael M. Zavlanos, Karl H. Johansson, Sandra Hirche
Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time.
no code implementations • 23 Feb 2024 • Zhe Jiao, Xiaoyan Luo, Xinlei Yi
The aim of this work is to develop deep learning-based algorithms for high-dimensional stochastic control problems based on physics-informed learning and dynamic programming.
no code implementations • 31 May 2023 • Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie, Yiguang Hong, Tianyou Chai, Karl H. Johansson
Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to $\mathcal{O}(\log(T))$, and the network cumulative constraint violation bound is reduced to $\mathcal{O}(\sqrt{\log(T)T})$ and $\mathcal{O}(\log(T))$ without and with Slater's condition, respectively.
1 code implementation • 15 Jul 2021 • Ye Yuan, Jun Liu, Dou Jin, Zuogong Yue, Ruijuan Chen, Maolin Wang, Chuan Sun, Lei Xu, Feng Hua, Xin He, Xinlei Yi, Tao Yang, Hai-Tao Zhang, Shaochun Sui, Han Ding
Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication pressure and high vulnerability when there exists a failure at or attack on the central client.
no code implementations • 9 Jun 2021 • Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie, Tianyou Chai, Karl H. Johansson
A novel algorithm is first proposed and it achieves an $\mathcal{O}(T^{\max\{c, 1-c\}})$ bound for static regret and an $\mathcal{O}(T^{(1-c)/2})$ bound for cumulative constraint violation, where $c\in(0, 1)$ is a user-defined trade-off parameter, and thus has improved performance compared with existing results.
no code implementations • 1 May 2021 • Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie, Tianyou Chai, Karl H. Johansson
This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions.
no code implementations • 4 Jun 2020 • Xinlei Yi, Shengjun Zhang, Tao Yang, Tianyou Chai, Karl H. Johansson
The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of $n$ local cost functions by using local information exchange is considered.
Optimization and Control
no code implementations • 6 Mar 2019 • Xinlei Yi, Xiuxian Li, Lihua Xie, Karl H. Johansson
Assuming Slater's condition, we show that the algorithm achieves smaller bounds on the constraint violation.
no code implementations • 2 Apr 2016 • Ren Zheng, Xinlei Yi, Wenlian Lu, Tianping Chen
In this paper, we investigate stability of a class of analytic neural networks with the synaptic feedback via event-triggered rules.