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Randomized algorithms in numerical linear algebra
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DOI：10.1017/S0962492917000058.png)
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Abstract
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This survey provides an introduction to the use of randomization in the design of fast algorithms for numerical linear algebra. These algorithms typically examine only a subset of the input to solve basic problems approximately, including matrix multiplication, regression and low-rank approximation. The survey describes the key ideas and gives complete proofs of the main results in the field. A central unifying idea is sampling the columns (or rows) of a matrix according to their squared lengths.
Keywords:
MONTE-CARLO ALGORITHMS
LARGE MATRICES
APPROXIMATION
COMPUTATION
JOHNSON
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