ASKIT: An Efficient, Parallel Library for High-Dimensional Kernel Summations

DOI10.1137/15M1026468zbMath1416.65578MaRDI QIDQ2830640

Bo Xiao, George Biros, William B. March, Chenhan D. Yu

Publication date: 28 October 2016

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

zbMATH Keywords

linear algebramachine learningkernel machinestreecodes\(N\)-body methodsrandomized matrix approximation

Mathematics Subject Classification ID

Related Items (5)

Far-field compression for fast kernel summation methods in high dimensions ⋮ Algorithmic patterns for \(\mathcal {H}\)-matrices on many-core processors ⋮ Fast Approximation of the Gauss--Newton Hessian Matrix for the Multilayer Perceptron ⋮ ASKIT ⋮ Unnamed Item

Uses Software

• UCI-ml

Cites Work

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• On the Compression of Low Rank Matrices
• A Fast Summation Tree Code for Matérn Kernel
• Fast monte-carlo algorithms for finding low-rank approximations
• Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication
• The Fast Gauss Transform
• A fast algorithm for particle simulations

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