The problem is to estimate x based on observed values

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Unformatted text preview: To be sure Matlab knows your matrix is tridiagonal, you can declare the matrix as sparse, using spdiags, which can be used to create a tridiagonal matrix. You could also create the tridiagonal matrix conventionally, and then convert the resulting matrix to a sparse one using sparse. One other thing you need to know. Suppose you need to solve a group of linear equations with the same coefficient matrix, i.e., you need to compute F −1 a1 , ..., F −1 am , where F is invertible and ai are column vectors. By concatenating columns, this can be expressed as a single matrix F −1 a 1 · · · F −1 a m = F −1 [ a 1 · · · a m ] . To compute this matrix using Matlab, you should collect the righthand sides into one matrix (as above) and use Matlab’s backslash operator: F\A. This will do the right thing: factor the matrix F once, and carry out multiple back substitutions for the righthand sides. 8.6 Newton method for approximate total variation de-noising. Total variation de-noising is based on the bi-criterion problem with the two objectives n −1 x − xcor 2...
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This note was uploaded on 09/10/2013 for the course C 231 taught by Professor F.borrelli during the Fall '13 term at Berkeley.

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