{"title":"Optimization of the FIND Algorithm to Compute the Inverse of a Sparse Matrix","authors":"S. Li, Eric F Darve","doi":"10.1109/IWCE.2009.5091136","DOIUrl":null,"url":null,"abstract":"The FIND algorithm is a fast algorithm designed to calculate entries of the inverse of a sparse matrix. Such calculation is critical in many applications, e.g., quantum transport in nano-devices. For a 2D device discretized as N times N mesh, the best known algorithms have a running time of O(N 4 ), whereas FIND only requires O(N 3 ), although with a larger constant factor. By exploiting the extra sparsity and symmetry, the size of the problem where FIND becomes faster than others may decrease from a 130 times 130 mesh down to a 40 times 40 mesh. This improvement will make the optimized FIND algorithm appealing to small problems as well, thus becoming competitive for most real applications.","PeriodicalId":443119,"journal":{"name":"2009 13th International Workshop on Computational Electronics","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 13th International Workshop on Computational Electronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWCE.2009.5091136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The FIND algorithm is a fast algorithm designed to calculate entries of the inverse of a sparse matrix. Such calculation is critical in many applications, e.g., quantum transport in nano-devices. For a 2D device discretized as N times N mesh, the best known algorithms have a running time of O(N 4 ), whereas FIND only requires O(N 3 ), although with a larger constant factor. By exploiting the extra sparsity and symmetry, the size of the problem where FIND becomes faster than others may decrease from a 130 times 130 mesh down to a 40 times 40 mesh. This improvement will make the optimized FIND algorithm appealing to small problems as well, thus becoming competitive for most real applications.