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Rates of Convergence for Empirical Spectral Measures: A Soft Approach

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Convexity and Concentration

Part of the book series: The IMA Volumes in Mathematics and its Applications ((IMA,volume 161))

Abstract

Understanding the limiting behavior of eigenvalues of random matrices is the central problem of random matrix theory. Classical limit results are known for many models, and there has been significant recent progress in obtaining more quantitative, non-asymptotic results. In this paper, we describe a systematic approach to bounding rates of convergence and proving tail inequalities for the empirical spectral measures of a wide variety of random matrix ensembles. We illustrate the approach by proving asymptotically almost sure rates of convergence of the empirical spectral measure in the following ensembles: Wigner matrices, Wishart matrices, Haar-distributed matrices from the compact classical groups, powers of Haar matrices, randomized sums and random compressions of Hermitian matrices, a random matrix model for the Hamiltonians of quantum spin glasses, and finally the complex Ginibre ensemble. Many of the results appeared previously and are being collected and described here as illustrations of the general method; however, some details (particularly in the Wigner and Wishart cases) are new.

Our approach makes use of techniques from probability in Banach spaces, in particular concentration of measure and bounds for suprema of stochastic processes, in combination with more classical tools from matrix analysis, approximation theory, and Fourier analysis. It is highly flexible, as evidenced by the broad list of examples. It is moreover based largely on “soft” methods, and involves little hard analysis.

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Acknowledgements

This research was partially supported by grants from the U.S. National Science Foundation (DMS-1308725 to E.M.) and the Simons Foundation (#315593 to M.M.). This paper is an expansion of the first-named author’s talk at the excellent workshop “Information Theory and Concentration Phenomena” at the Institute for Mathematics and its Applications, as part of the IMA Thematic Year on Discrete Structures: Analysis and Applications. The authors thank the IMA for its hospitality.

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Authors and Affiliations

  1. Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, 10900 Euclid Ave., Cleveland, OH, 44106, USA

    Elizabeth S. Meckes & Mark W. Meckes

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  1. Elizabeth S. Meckes
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  2. Mark W. Meckes
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Correspondence to Mark W. Meckes .

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Editors and Affiliations

  1. Department of Mathematics, Rutgers University, Piscataway, New Jersey, USA

    Eric Carlen

  2. Department of Mathematical Sciences, University of Delaware, Newark, Delaware, USA

    Mokshay Madiman

  3. Department of Mathematics, Case Western Reserve University, Cleveland, Ohio, USA

    Elisabeth M. Werner

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Meckes, E.S., Meckes, M.W. (2017). Rates of Convergence for Empirical Spectral Measures: A Soft Approach. In: Carlen, E., Madiman, M., Werner, E. (eds) Convexity and Concentration. The IMA Volumes in Mathematics and its Applications, vol 161. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7005-6_5

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