Lecture 4 : Concentration and Matrix Multiplication
نویسنده
چکیده
Today, we will continue with our discussion of scalar and matrix concentration, with a discussion of the matrix analogues of Markov’s, Chebychev’s, and Chernoff’s Inequalities. Then, we will return to bounding the error for our approximating matrix multiplication algorithm. We will start with using Hoeffding-Azuma bounds from last class to get improved Frobenius norm bounds, and then (next time) we will describe how to use the matrix concentration results to get spectral norm bounds for approximate multiplication.
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