Heng Cao, Haifeng Xi, et al.
WSC 2003
This article reviews recent advances in convex optimization algorithms for big data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniques such as first-order methods and randomization for scalability, and survey the important role of parallel and distributed computation. The new big data algorithms are based on surprisingly simple principles and attain staggering accelerations even on classical problems. © 2014 IEEE.
Heng Cao, Haifeng Xi, et al.
WSC 2003
Shu Tezuka
WSC 1991
Hang-Yip Liu, Steffen Schulze, et al.
Proceedings of SPIE - The International Society for Optical Engineering
Corneliu Constantinescu
SPIE Optical Engineering + Applications 2009