Randomized algorithms for very large matrix problems have received
a great deal of attention in recent years. Much of this work was
motivated by problems in large-scale data analysis, largely since
matrices are popular structures with which to model data drawn from
a wide range of application domains, and the success of this line
of work opens the possibility of performing matrix-based
computations with truly massive data sets. Originating within
theoretical computer science, this work was subsequently extended
and applied in important ways by researchers from numerical linear
algebra, statistics, applied mathematics, data analysis, and
machine learning, as well as domain scientists. Randomized
Algorithms for Matrices and Data provides a detailed overview,
appropriate for both students and researchers from all of these
areas, of recent work on the theory of randomized matrix algorithms
as well as the application of those ideas to the solution of
practical problems in large-scale data analysis. By focusing on
ubiquitous and fundamental problems such as least-squares
approximation and low-rank matrix approximation that have been at
the center of recent developments, an emphasis is placed on a few
simple core ideas that underlie not only recent theoretical
advances but also the usefulness of these algorithmic tools in
large-scale data applications.
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