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- W2568803847 endingPage "343" @default.
- W2568803847 startingPage "301" @default.
- W2568803847 abstract "Compressive sensing and sparse approximation have many emerging applications, and are a relatively new driving force for the development of splitting methods in optimization. Many sparse coding problems are well described by variational models with ℓ 1-norm penalties and constraints that are designed to promote sparsity. Successful algorithms need to take advantage of the separable structure of potentially complicated objectives by “splitting” them into simpler pieces and iteratively solving a sequence of simpler convex minimization problems. In particular, isolating ℓ 1 terms from the rest of the objective leads to simple soft thresholding updates or ℓ 1 ball projections. A few basic splitting techniques can be used to design a huge variety of relevant algorithms. This chapter will focus on operator splitting strategies that are based on proximal operators, duality, and alternating direction methods. These will be explained in the context of basis pursuit variants and through compressive sensing applications." @default.
- W2568803847 created "2017-01-13" @default.
- W2568803847 creator A5052123856 @default.
- W2568803847 creator A5060687985 @default.
- W2568803847 date "2016-01-01" @default.
- W2568803847 modified "2023-09-26" @default.
- W2568803847 title "Operator Splitting Methods in Compressive Sensing and Sparse Approximation" @default.
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