2011

Tail bounds for all eigenvalues of a sum of random matrices

Gittens, Alex, Tropp, Joel A.

Understand

This work introduces the minimax Laplace transform method, a modification of the cumulant-based matrix Laplace transform method developed in "User-friendly tail bounds for sums of random matrices" (arXiv:1004.4389v6) that yields both upper and lower bounds on each eigenvalue of a sum of random self-adjoint matrices.

  • This machinery is used to derive eigenvalue analogues of the classical Chernoff, Bennett, and Bernstein bounds.
  • Two examples demonstrate the efficacy of the minimax Laplace transform.
  • The first concerns the effects of column sparsification on the spectrum of a matrix with orthonormal rows.

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