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2010

Approximation accuracy, gradient methods, and error bound for structured convex optimization

13 years 10 months ago
Approximation accuracy, gradient methods, and error bound for structured convex optimization
Convex optimization problems arising in applications, possibly as approximations 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) solution of the original noiseless problem. Related to this is an error bound for the linear 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. Key words. Convex optimization, compressed sensing, ℓ1-regularization, nuclear/trace norm, regression, variable selection, sensor network localization, approximation accuracy, proximal gradient method, error bound, linear convergence.
Paul Tseng
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where MP
Authors Paul Tseng
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