Abstract
The notion of L p sampling, and corresponding algorithms known as L p samplers, has found a wide range of applications in the design of data stream algorithms and beyond. In this survey, we present some of the core algorithms to achieve this sampling distribution based on ideas from hashing, sampling, and sketching. We give results for the special cases of insertion-only inputs, lower bounds for the sampling problems, and ways to efficiently sample multiple elements. We describe a range of applications of L p sampling, drawing on problems across the domain of computer science, from matrix and graph computations, as well as to geometric and vector streaming problems.
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