Randomized Numerical Linear Algebra (RandNLA) is a new field introduced in the first Workshop on "Randomized Numerical Linear Algebra (RandNLA): Theory and Practice" as follows:
Matrix algorithms are the foundation for many methods in data analysis, scientific computing, and engineering applications, and Numerical Linear Algebra (NLA) is the key discipline enabling the analysis and implementation of such algorithms. Without highly efficient and scalable NLA algorithms, the challenge of big data will not be met. To meet this challenge a true boost in performance is necessary, and that necessitates new algorithmic paradigms to be developed, analyzed, and deployed. Randomization is one of only a handful of paradigms that have the potential to deliver the desired true boost in performance. In the past, scientists and application users were distrustful and shied away from randomized numerical linear algebra algorithms, because (i) their output is unpredictable, (ii) it may not be reproducible, (iii) the error analysis is probabilistic, and (iv) the obtained accuracy is very crude. However, the NLA community is now seriously considering the use of "radical" ideas like randomization, and, indeed, recent years saw much research on randomized numerical linear algebra algorithms. These practical developments represent huge opportunities for the theoretical computer science community: how can randomization and sampling be leveraged in order to design faster numerical algorithms? The goal of the workshop is to expose the participants to recent progress on developing randomized numerical linear algebra algorithms, as well as on the application of such algorithms to a variety of disciplines and domains.
All the entries ofeaturing papers on RandNLA are listed under the RandNLA tag. Of related interest is this page: Randomized Numerical Linear Algebra for Large Scale Data Analysis