8 papers · ranked by Valyu relevance
Artin Spiridonoff, Alex Olshevsky, Ioannis Ch. Paschalidis
We consider the standard model of distributed optimization of a sum of functions $F(z)=\sum_{i=1}^{n}f_{i}(z)$, where node i in a network holds the function fi(z). We allow for a harsh network model characterized by asynchronous updates, message delays, unpredictable message losses, and directed communication among…
Ren Xiaoguang, Xu Xinhai
Computational fluid dynamics (CFD) simulation often needs to periodically output intermediate results to files in the form of snapshots for visualization or restart, which seriously impacts the performance. In this paper, we present asynchronous pipeline I/O (AP-IO) optimization scheme for the periodically snapshot…
Trevor S Frisby, Zhiyun Gong, Christopher James Langmead
Bayesian Optimization is a sequential strategy for optimizing black-box objective functions, f. As mentioned in the introduction, Gaussian Processes (GP) are frequently used to represent and compute over the distribution P(f). A GP is defined by a mean function $μ:X\rightarrowR$ and kernel function…
J Kyle Medley, Shaik Asifullah, Joseph Hellerstein, Herbert M Sauro
Mechanistic kinetic models of biological pathways are an important tool for understanding biological systems. Constructing kinetic models requires fitting the parameters to experimental data. However, parameter fitting on these models is a non–convex, non–linear optimization problem. Many algorithms have been proposed…
Kuiwu Wang, Qin Zhang, Guimei Zheng, Xiaolong Hu + 1 more
Aiming at the problem of asynchronous multi-target tracking, this paper studies the AA fusion optimization problem of multi-sensor networks. Firstly, each sensor node runs a PHD filter, and the measurement information obtained from different sensor nodes in the fusion interval is flood communicated into composite…
Haijie Pan, Lirong Zheng, Sylvain Girard
Machine learning models often converge slowly and are unstable due to the significant variance of random data when using a sample estimate gradient in SGD. To increase the speed of convergence and improve stability, a distributed SGD algorithm based on variance reduction, named DisSAGD, is proposed in this study.…
Zuhan Liu, Kexin Zhao, Xuehu Liu, Huan Xu
With the rapid expansion of industrialization and urbanization, fine Particulate Matter (PM2.5) pollution has escalated into a major global environmental crisis. This pollution severely affects human health and ecosystem stability. Accurately predicting PM2.5 levels is essential. However, air quality forecasting…
Peter L. Bartlett, Chris Junchi Li, Jingfeng Wu, Bin Yu
In the field of optimization, developing accelerated methods for solving minimax and fixed-point problems remains a fundamental challenge. This paper presents a novel family of dual accelerated algorithms that achieve optimal convergence rates for both minimax and fixed-point problems. By exploring new anchoring…