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Conditioning of the matrix-matrix exponentiation

Published: 01 October 2018 Publication History

Abstract

If A has no eigenvalues on the closed negative real axis, and B is arbitrary square complex, the matrix-matrix exponentiation is defined as AB := elog(A)B. It arises, for instance, in Von Newmann's quantum-mechanical entropy, which in turn finds applications in other areas of science and engineering. In this paper, we revisit this function and derive new related results. Particular emphasis is devoted to its Fréchet derivative and conditioning. We propose a new definition of bivariate matrix function and derive some general results on their Fréchet derivatives, which hold, not only to the matrix-matrix exponentiation but also to other known functions, such as means of two matrices, second order Fréchet derivatives and some iteration functions arising in matrix iterative methods. The numerical computation of the Fréchet derivative is discussed and an algorithm for computing the relative condition number of ABis proposed. Some numerical experiments are included.

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      Published In

      cover image Numerical Algorithms
      Numerical Algorithms  Volume 79, Issue 2
      October 2018
      312 pages

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      Springer-Verlag

      Berlin, Heidelberg

      Publication History

      Published: 01 October 2018

      Author Tags

      1. Bivariate matrix function
      2. Conditioning
      3. Fréchet derivative
      4. Matrix exponential
      5. Matrix logarithm
      6. Matrix-matrix exponentiation

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