The QR Algorithm revisited Export

@article{citeulike:1451443, abstract = {The QR algorithm is still one of the most important methods for computing eigenvalues and eigenvectors of matrices. Most discussions of the QR algorithm begin with a very basic version and move by steps toward the versions of the algorithm that are actually used. This paper outlines a pedagogical path that leads directly to the implicit multishift QR algorithms that are used in practice, bypassing the basic QR algorithm completely.}, address = {Pullman, Washington 99164-3113}, author = {Watkins, David S.}, citeulike-article-id = {1451443}, citeulike-linkout-0 = {http://www.math.wsu.edu/faculty/watkins/pdfiles/qrevisit.pdf}, journal = {SIAM Review}, keywords = {eigenvalue, eigenvector, hessenberg, qr}, organization = {Department of Mathematics, Washington State University}, posted-at = {2007-07-12 09:18:48}, priority = {2}, title = {The QR Algorithm revisited}, url = {http://www.math.wsu.edu/faculty/watkins/pdfiles/qrevisit.pdf} }

TY - JOUR ID - citeulike:1451443 L3 - citeulike-article-id:1451443 N2 - The QR algorithm is still one of the most important methods for computing eigenvalues and eigenvectors of matrices. Most discussions of the QR algorithm begin with a very basic version and move by steps toward the versions of the algorithm that are actually used. This paper outlines a pedagogical path that leads directly to the implicit multishift QR algorithms that are used in practice, bypassing the basic QR algorithm completely. JF - SIAM Review OR - Department of Mathematics, Washington State University AD - Pullman, Washington 99164-3113 TI - The QR Algorithm revisited KW - eigenvalue KW - eigenvector KW - hessenberg KW - qr AU - Watkins, David UR - http://www.math.wsu.edu/faculty/watkins/pdfiles/qrevisit.pdf ER -