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Solving Least-Squares Problems with Gradient Descent: the Least Mean-Square Algorithm

May 9, 2019 by allsignal

Solving Least-Squares Problems with Gradient Descent: the Least Mean-Square Algorithm

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  1. Question 1 of 4
    1. Question

    Which of the following are disadvantages of directly computing the solution to the MMSE filtering or least squares problem using {\bf w} = {\bf R}^{-1}{\bf p}? Assume the dimension of {\bf w} is N and that L data samples are available. Select all that apply.

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  2. Question 2 of 4
    2. Question

    The LMS algorithm updates the current estimate of the weights with a step in a direction given by the negative gradient of the instantaneous squared error {\bf e}^2[n].

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  3. Question 3 of 4
    3. Question

    The instantaneous gradient used in the LMS algorithm is the same as the gradient of the MSE.

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  4. Question 4 of 4
    4. Question

    Define the error as e[n] = d[n]-{\bf x}^T[n]{\bf w}. Which of the following is the gradient of e^2[n]?

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