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 21 articles )

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  1. Frandi, Emanuele; Ñanculef, Ricardo; Lodi, Stefano; Sartori, Claudio; Suykens, Johan A.K.: Fast and scalable Lasso via stochastic Frank-Wolfe methods with a convergence guarantee (2016)
  2. Wang, Jie; Wonka, Peter; Ye, Jieping: Lasso screening rules via dual polytope projection (2015)
  3. Xiang, Shuo; Shen, Xiaotong; Ye, Jieping: Efficient nonconvex sparse group feature selection via continuous and discrete optimization (2015)
  4. Yang, Yi; Zou, Hui: A fast unified algorithm for solving group-lasso penalize learning problems (2015)
  5. Zhang, Hai-Bin; Jiang, Jiao-Jiao; Zhao, Yun-Bin: On the proximal Landweber Newton method for a class of nonsmooth convex problems (2015)
  6. Zou, Jian; Fu, Yuli: Split Bregman algorithms for sparse group lasso with application to MRI reconstruction (2015)
  7. Zou, Jian; Fu, Yuli; Zhang, Qiheng; Li, Haifeng: Split Bregman algorithms for multiple measurement vector problem (2015)
  8. Chen, Jianhui; Zhou, Jiayu; Ye, Jieping: Low-rank and sparse multi-task learning (2014)
  9. Li, Leijun; Hu, Qinghua; Wu, Xiangqian; Yu, Daren: Exploration of classification confidence in ensemble learning (2014)
  10. Lin, Xiaodong; Pham, Minh; Ruszczyński, Andrzej: Alternating linearization for structured regularization problems (2014)
  11. Paramanand, C.; Rajagopalan, A.N.: Shape from sharp and motion-blurred image pair (2014)
  12. 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)
  13. Lu, Zhaosong; Zhang, Yong: Sparse approximation via penalty decomposition methods (2013)
  14. Qin, Zhiwei; Scheinberg, Katya; Goldfarb, Donald: Efficient block-coordinate descent algorithms for the group Lasso (2013)
  15. Yang, Junfeng; Yuan, Xiaoming: Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization (2013)
  16. Yang, Yang; Huang, Zi; Yang, Yi; Liu, Jiajun; Shen, Heng Tao; Luo, Jiebo: Local image tagging via graph regularized joint group sparsity (2013)
  17. 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)
  18. Herzog, Roland; Stadler, Georg; Wachsmuth, Gerd: Directional sparsity in optimal control of partial differential equations (2012)
  19. Sra, Suvrit: Fast projections onto mixed-norm balls with applications (2012)
  20. Zhang, Limei; Chen, Songcan; Qiao, Lishan: Graph optimization for dimensionality reduction with sparsity constraints (2012)

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