Multishift variants of the QZ algorithm with aggressive early deflation
New variants of the QZ algorithm for solving the generalized eigenvalue problem are proposed. An extension of the small-bulge multishift QR algorithm is developed, which chases chains of many small bulges instead of only one bulge in each QZ iteration. This allows the effective use of level 3 BLAS operations, which in turn can provide efficient utilization of high performance computing systems with deep memory hierarchies. Moreover, an extension of the aggressive early deflation strategy is proposed, which can identify and de. ate converged eigenvalues long before classic deflation strategies would. Consequently, the number of overall QZ iterations needed until convergence is considerably reduced. As a third ingredient, we reconsider the deflation of infinite eigenvalues and present a new deflation algorithm, which is particularly effective in the presence of a large number of infinite eigenvalues. Combining all these developments, our implementation significantly improves existing implementations of the QZ algorithm. This is demonstrated by numerical experiments with random matrix pairs as well as with matrix pairs arising from various applications.
Keywords: generalized eigenvalue problem ; generalized Schur form ; QZ algorithm ; multishifts ; aggressive early deflation ; blocked algorithms ; Generalized Eigenvalue Problem ; Arbitrary Pencil-A ; Qr Algorithm ; Schur Decomposition ; Robust Software ; Level-3 Blas ; Error-Bounds ; Lambda-B ; Performance ; Shifts
Record created on 2011-05-05, modified on 2016-08-09