 Memory-usage advantageous block recursive matrix inverseIria C.S. Cosme, Isaac F. Fernandes, João L. de Carvalho and Samuel Xavier-de-Souza Applied Mathematics and Computation, 2018, vol. 328, issue C, 125-136 Abstract: The inversion of extremely high order matrices has been a challenging task because of the limited processing and memory capacity of conventional computers. In a scenario in which the data does not fit in memory, it is worth to consider exchanging less memory usage for more processing time in order to enable the computation of the inverse which otherwise would be prohibitive. We propose a new algorithm to compute the inverse of block partitioned matrices with a reduced memory footprint. The algorithm works recursively to invert one block of a k × k block matrix M, with k ≥ 2, based on the successive splitting of M. It computes one block of the inverse at a time, in order to limit memory usage during the entire processing. Experimental results show that, despite increasing computational complexity, matrices that otherwise would exceed the memory-usage limit can be inverted using this technique. Keywords: Block matrices; Large matrices; Schur complement; Recursive matrix inversion; Low memory usage (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:328:y:2018:i:c:p:125-136 DOI: 10.1016/j.amc.2018.01.051 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
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