Optimal rank matrix algebras preconditioners

F. Tudisco, C. Di Fiore, E. E. Tyrtyshnikov

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


When a linear system Ax=y is solved by means of iterative methods (mainly CG and GMRES) and the convergence rate is slow, one may consider a preconditioner P and move to the preconditioned system P-1Ax=P-1y. The use of such preconditioner changes the spectrum of the matrix defining the system and could result into a great acceleration of the convergence rate. The construction of optimal rank preconditioners is strongly related to the possibility of splitting A as A=P+R+E, where E is a small perturbation and R is of low rank (Tyrtyshnikov, 1996) [1]. In the present work we extend the black-dot algorithm for the computation of such splitting for P circulant (see Oseledets and Tyrtyshnikov, 2006 [2]), to the case where P is in A, for several known low-complexity matrix algebras A. The algorithm so obtained is particularly efficient when A is Toeplitz plus Hankel like. We finally discuss in detail the existence and the properties of the decomposition A=P+R+E when A is Toeplitz, also extending to the φ-circulant and Hartley-type cases some results previously known for P circulant.
Original languageEnglish
Pages (from-to)405-427
Number of pages23
JournalLinear Algebra and its Applications
Issue number1
Early online date19 Sep 2012
Publication statusPublished - 1 Jan 2013


  • preconditioning
  • matrix algebras
  • Toeplitz
  • Hankel
  • clustering
  • fast discrete transforms

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