Randomized methods for matrix computations
Provides an accessible overview of randomized matrix factorization methods for researchers and practitioners needing efficient large-scale matrix computations.
This work introduces randomized algorithms for matrix factorizations that use random projections to reduce dimensionality, achieving high speed and low communication costs, especially for low-rank approximations of large matrices.
The purpose of this text is to provide an accessible introduction to a set of recently developed algorithms for factorizing matrices. These new algorithms attain high practical speed by reducing the dimensionality of intermediate computations using randomized projections. The algorithms are particularly powerful for computing low-rank approximations to very large matrices, but they can also be used to accelerate algorithms for computing full factorizations of matrices. A key competitive advantage of the algorithms described is that they require less communication than traditional deterministic methods.