English
资源说明：Convex optimization problems arising in applications, possibly as approx-
imations of intractable problems, are often structured and large scale. When the data
are noisy, it is of interest to bound the solution error relative to the (unknown) solu-
tion of the original noiseless problem. Related to this is an error bound for the lin-
ear convergence analysis of first-order gradient methods for solving these problems.
Example applications include compressed sensing, variable selection in regression,
TV-regularized image denoising, and sensor network localization
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