November 2015
Intermediate to advanced
328 pages
10h 12m
English
Content preview from Parallel Computing for Data Science
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CONTENTS xvii
12.6 Example: Graph Connectedness . . . . . . . . . . . . . . . . 264
12.6.1 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 264
12.6.2 The “Log Trick” . . . . . . . . . . . . . . . . . . . . 266
12.6.3 Parallel Computation . . . . . . . . . . . . . . . . . 266
12.6.4 The matpow Package . . . . . . . . . . . . . . . . . 267
12.6.4.1 Features . . . . . . . . . . . . . . . . . . . 267
12.7 Solving Systems of Linear Equations . . . . . . . . . . . . . 267
12.7.1 The Classical Approach: Gaussian Elimination and
the LU Decomposition . . . . . . . . . . . . . . . . . 268
12.7.2 The Jacobi Algorithm . . . . . . . . . . . . . . . .
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