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• W2912651269 startingPage "55" @default.
• W2912651269 abstract "This chapter reviews the representative optimization algorithms for solving low-rank models. They are categorized into convex algorithms, nonconvex algorithms, and randomized algorithms. The convex algorithms include accelerated proximal gradient (APG), Frank–Wolfe algorithm, alternating direction method (ADM) and its variants, linearized alternating direction method with adaptive penalty (LADMAP, for the two-block case) and proximal linearized alternating direction method with parallel splitting and adaptive penalty (PLADMPSAP, for the case of more than two blocks). The nonconvex algorithms include generalized singular value thresholding (GSVT), iteratively reweighted nuclear norm (IRNN) algorithm, truncated nuclear norm minimization, iteratively reweighted least squares (IRLS), and factorization method. The randomized algorithms include ℓ1 filtering, ℓ2,1 filtering, the Reduce & Express algorithm for solving relaxed robust Low-Rank Representation, and a randomized algorithm for Online Matrix Completion. The convergence/convergence rate problems are studied when appropriate." @default.
• W2912651269 created "2019-02-21" @default.
• W2912651269 creator A5016399094 @default.
• W2912651269 date "2017-01-01" @default.
• W2912651269 modified "2023-10-18" @default.
• W2912651269 title "Optimization Algorithms" @default.
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• W2912651269 doi "https://doi.org/10.1016/b978-0-12-812731-5.00004-4" @default.
• W2912651269 hasPublicationYear "2017" @default.
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