Convex Optimization for Big Data
It addresses scalability challenges in optimization for Big Data applications, but is incremental as it reviews existing techniques.
The paper reviews recent advances in convex optimization algorithms for Big Data, focusing on reducing computational, storage, and communications bottlenecks, and notes that these algorithms achieve significant accelerations on classical problems.
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 like 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.