2012

Inverses of symmetric, diagonally dominant positive matrices and applications

Hillar, Christopher J., Lin, Shaowei, Wibisono, Andre

Understand

We prove tight bounds for the $\infty$-norm of the inverse of symmetric, diagonally dominant positive matrices.

  • We also prove a new lower-bound form of Hadamard's inequality for the determinant of diagonally dominant positive matrices and an improved upper bound for diagonally balanced positive matrices.
  • Applications of our results include numerical stability for linear systems, bounds on inverses of differentiable functions, and consistency of the maximum likelihood equations for maximum entropy graph distributions.

Built on

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Similar

  • P.N. Shivakumar, J.J. Williams, Q. Ye, and C.A. Marinov. On two-sided bounds related to weakly diagonally dominant M M -matrices with application to digital circuit dynamics

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  • J.R. Munkres. Topology (2nd Edition)

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  • C.R. Johnson and C. Hillar. Eigenvalues of words in two positive definite letters

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  • D.A. Spielman and S.H. Teng. Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems

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  • D.A. Spielman and S.H. Teng. Nearly-linear time algorithms for preconditioning and solving symmetric, diagonally dominant linear systems

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  • R. Sanyal, B. Sturmfels, C. Vinzant. The entropic discriminant

    Cited in the paper.

Then

  • W. Li. The infinity norm bound for the inverse of nonsingular diagonal dominant matrices

    2008

    Later among the works it cites.

  • D. Cvetković and S. Simić. Towards a spectral theory of graphs based on the signless Laplacian, III

    2010

    Later among the works it cites.

  • D.A. Spielman. Algorithms, graph theory, and linear equations in Laplacian matrices

    2010

    Later among the works it cites.

  • S. Chatterjee, P. Diaconis, A. Sly. Random graphs with a given degree sequence

    2011

    Later among the works it cites.

  • C. Hillar and A. Wibisono. Maximum entropy distributions on graphs

    Original

    2013

    Closest in time.

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