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Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems

Published:13 June 2004Publication History

ABSTRACT

We present algorithms for solving symmetric, diagonally-dominant linear systems to accuracy ε in time linear in their number of non-zeros and log (κf (A) ε), where κf (A) is the condition number of the matrix defining the linear system. Our algorithm applies the preconditioned Chebyshev iteration with preconditioners designed using nearly-linear time algorithms for graph sparsification and graph partitioning.

References

  1. D. Achlioptas and F. McSherry. Fast computation of low rank matrix approximations. In 33rd ACM STOC, pages 611--618, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. N. Alon, R. M. Karp, D. Peleg, and D. West. A graph-theoretic game and its application to the k -server problem. SIAM Journal on Computing, 24(1):78--100, Feb. 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. A. A. Benczur and D. R. Karger. Approximating s-t minimum cuts in O(n2) time. In 28th ACM STOC, pages 47--55, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. M. Bern, J. Gilbert, B. Hendrickson, N. Nguyen, and S. Toledo. Support-graph preconditioners. submitted to SIAM J. Matrix Anal. & Appl. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. E. Boman, D. Chen, B. Hendrickson, and S. Toledo. Maximum-weight-basis preconditioners. to appear in Numerical Linear Algebra and Applications.Google ScholarGoogle Scholar
  6. E. Boman and B. Hendrickson. Support theory for preconditioning. submitted to SIAM J. Matrix Anal. & Appl (Revised 10/02). Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. E. Boman and B. Hendrickson. On spanning tree preconditioners. Manuscript, Sandia National Lab., 2001.Google ScholarGoogle Scholar
  8. W. E. Donath and A. J. Hoffman. Algorithms for partitioning graphs and computer logic based on eigenvectors of connection matrices. IBM Technical Disclosure Bulletin, 15(3):938--944, 1972.Google ScholarGoogle Scholar
  9. W. E. Donath and A. J. Hoffman. Lower bounds for the partitioning of graphs. IBM Journal of Research and Development, 17(5):420--425, Sept. 1973.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. A. Frieze, R. Kannan, and S. Vempala. Fast Monte-Carlo algorithms for finding low-rank approximations. In 39th IEEE FOCS, pages 370--378, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Z. Furedi and J. Komlos. The eigenvalues of random symmetric matrices. Combinatorica, 1(3):233--241, 1981.Google ScholarGoogle ScholarCross RefCross Ref
  12. G. H. Golub and M. Overton. The convergence of inexact Chebychev and Richardson iterative methods for solving linear systems. Numerische Mathematik, 53:571--594, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. K. Gremban. Combinatorial Preconditioners for Sparse, Symmetric, Diagonally Dominant Linear Systems. PhD thesis, Carnegie Mellon University, CMU-CS-96-123, 1996.Google ScholarGoogle Scholar
  14. K. Gremban, G. Miller, and M. Zagha. Performance evaluation of a new parallel preconditioner. In 9th IPPS, pages 65--69, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. B. Hendrickson and R. Leland. The Chaco user's guide, version 2. 0. Tech. Rep. SAND94-2692, Sandia National Labs, Albuquerque, NM, Oct. 1994.Google ScholarGoogle Scholar
  16. A. Joshi. Topics in Optimization and Sparse Linear Systems. PhD thesis, UIUC, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. G. Karypis and V. Kumar. MeTis: Unstructured Graph Partitioning and Sparse Matrix Ordering System, Version 4. 0, Sept. 1998.Google ScholarGoogle Scholar
  18. T. Leighton and S. Rao. Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms. Journal of the ACM, 46(6):787--832, Nov. 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. R. J. Lipton, D. J. Rose, and R. E. Tarjan. Generalized nested dissection. SIAM Journal on Numerical Analysis, 16(2):346--358, Apr. 1979.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Lovasz and Simonovits. Random walks in a convex body and an improved volume algorithm. RSA: Random Structures & Algorithms, 4:359--412, 1993.Google ScholarGoogle ScholarCross RefCross Ref
  21. B. M. Maggs, G. L. Miller, O. Parekh, R. Ravi, and S. L. M. Woo. Solving symmetric diagonally-dominant systems by preconditioning. 2002.Google ScholarGoogle Scholar
  22. J. Reif. Efficient approximate solution of sparse linear systems. Computers and Mathematics with Applications, 36(9):37--58, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  23. D. Spielman and S. -H. Teng. Solving sparse, symmetric, diagonally-dominant linear systems in time O(m1. 31). In 44th Annual IEEE FOCS, pages 416--427, 2003. Most recent version available at http://arxiv. org/cs. DS/0310036. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. D. A. Spielman and S. -H. Teng. Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems. available at http://arxiv. org/abs/cs. DS/0310051, 2003.Google ScholarGoogle Scholar
  25. P. M. Vaidya. Solving linear equations with symmetric diagonally dominant matrices by constructing good preconditioners. Unpublished manuscript UIUC 1990. A talk based on the manuscript was presented at the IMA Workshop on Graph Theory and Sparse Matrix Computation, October 1991, Minneapolis., 1990.Google ScholarGoogle Scholar

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        cover image ACM Conferences
        STOC '04: Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
        June 2004
        660 pages
        ISBN:1581138520
        DOI:10.1145/1007352

        Copyright © 2004 ACM

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        • Published: 13 June 2004

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