swMATH ID: 41749
Software Authors: Drineas, P.; Mahoney, M. W.
Description: RandNLA: Randomized Numerical Linear Algebra. Matrices are ubiquitous in computer science, statistics, and applied mathematics. An m × n matrix can encode information about m objects (each described by n features), or the behavior of a discretized differential operator on a finite element mesh; an n × n positive-definite matrix can encode the correlations between all pairs of n objects, or the edge-connectivity between all pairs of nodes in a social network; and so on. Motivated largely by technological developments that generate extremely large scientific and Internet datasets, recent years have witnessed exciting developments in the theory and practice of matrix algorithms. Particularly remarkable is the use of randomizationtypically assumed to be a property of the input data due to, for example, noise in the data generation mechanismsas an algorithmic or computational resource for the development of improved algorithms for fundamental matrix problems such as matrix multiplication, least-squares (LS) approximation, low-rank matrix approximation, and Laplacian-based linear equation solvers.
Homepage: https://www.stat.berkeley.edu/~mmahoney/pubs/RandNLA_in_CACM_2016.pdf
Related Software: Tensorlab; rsvd; TensorToolbox; ARPACK; RSVDPACK; TensorToolbox.jl; UTV; randUTV; MNIST; Algorithm 862; cross2D; BCLS; redbKIT; Algorithm 971; BSDS; tproduct; GitHub; CVXPY; Gurobi; OSUMC
Cited in: 25 Documents

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