SLEP: Sparse Learning with Efficient Projections. Main Features: 1) First-Order Method. At each iteration, we only need to evaluate the function value and the gradient; and thus the algorithms can handle large-scale sparse data. 2) Optimal Convergence Rate. The convergence rate O(1/k2) is optimal for smooth convex optimization via the first-order black-box methods. 3) Efficient Projection. The projection problem (proximal operator) can be solved efficiently. 4) Pathwise Solutions. The SLEP package provides functions that efficiently compute the pathwise solutions corresponding to a series of regularization parameters by the “warm-start” technique.

References in zbMATH (referenced in 34 articles )

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  4. Paramanand, C.; Rajagopalan, A. N.: Shape from sharp and motion-blurred image pair (2014) ioport
  5. Zhang, Haibin; Wei, Juan; Li, Meixia; Zhou, Jie; Chao, Miantao: On proximal gradient method for the convex problems regularized with the group reproducing kernel norm (2014)
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  7. Qin, Zhiwei; Scheinberg, Katya; Goldfarb, Donald: Efficient block-coordinate descent algorithms for the group Lasso (2013)
  8. Yang, Junfeng; Yuan, Xiaoming: Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization (2013)
  9. Yang, Yang; Huang, Zi; Yang, Yi; Liu, Jiajun; Shen, Heng Tao; Luo, Jiebo: Local image tagging via graph regularized joint group sparsity (2013)
  10. Zhang, Haibin; Jiang, Jiaojiao; Luo, Zhi-Quan: On the linear convergence of a proximal gradient method for a class of nonsmooth convex minimization problems (2013)
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  14. Mazumder, Rahul; Hastie, Trevor; Tibshirani, Robert: Spectral regularization algorithms for learning large incomplete matrices (2010)