PRKP: A Parallel Randomized Iterative Algorithm for Solving Linear Systems

This paper proposes a distributed-memory parallel randomized iterative algorithm for solving linear systems, called the parallel randomized kaczmarz projection (PRKP) algorithm. The algorithm has the property of greedy sampling, alternating projection, and lazy approximation. We derive the alternati...

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Veröffentlicht in:	2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys) S. 244 - 249
Hauptverfasser:	Wang, Junjie, Tian, Min, Wang, Yinglong, He, Guoping, Liu, Tao
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.12.2022
Schlagworte:	alternating projection Approximation algorithms Costs distributed memory systems Estimation Iterative algorithms Linear systems overdetermined linear systems parallel algorithms parallel iterative methods Processor scheduling randomized projection Scheduling
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