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- W3133669662 abstract "Large-scale optimization problems that seek sparse solutions have become ubiquitous. They are routinely solved with various specialized first-order methods. Although such methods are often fast, they usually struggle with not-so-well-conditioned problems. In this paper, specialized variants of an interior point-proximal method of multipliers are proposed and analyzed for problems of this class. Computational experience on a variety of problems, namely, multiperiod portfolio optimization, classification of data coming from functional magnetic resonance imaging, restoration of images corrupted by Poisson noise, and classification via regularized logistic regression, provides substantial evidence that interior point methods, equipped with suitable linear algebra, can offer a noticeable advantage over first-order approaches." @default.
- W3133669662 created "2021-03-15" @default.
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- W3133669662 date "2022-11-01" @default.
- W3133669662 modified "2023-10-17" @default.
- W3133669662 title "Sparse Approximations with Interior Point Methods" @default.
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- W3133669662 doi "https://doi.org/10.1137/21m1401103" @default.
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