FPC_AS
FPC_AS (fixed-point continuation and active set) is a MATLAB solver for the l1-regularized least squares problem: A fast algorithm for sparse reconstruction based on shrinkage, subspace optimization, and continuation. We propose a fast algorithm for solving the ℓ 1 -regularized minimization problem min x∈ℝ n μ∥x∥ 1 +∥Ax-b∥ 2 2 for recovering sparse solutions to an undetermined system of linear equations Ax=b. The algorithm is divided into two stages that are performed repeatedly. In the first stage a first-order iterative “shrinkage” method yields an estimate of the subset of components of x likely to be nonzero in an optimal solution. Restricting the decision variables x to this subset and fixing their signs at their current values reduces the ℓ 1 -norm ∥x∥ 1 to a linear function of x. The resulting subspace problem, which involves the minimization of a smaller and smooth quadratic function, is solved in the second phase. Our code FPC_AS embeds this basic two-stage algorithm in a continuation (homotopy) approach by assigning a decreasing sequence of values to μ. This code exhibits state-of-the-art performance in terms of both its speed and its ability to recover sparse signals
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References in zbMATH (referenced in 66 articles , 1 standard article )
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Sorted by year (- Bian, Fengmiao; Zhang, Xiaoqun: A three-operator splitting algorithm for nonconvex sparsity regularization (2021)
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- Cheng, Wanyou; Chen, Zixin; Hu, Qingjie: An active set Barzilar-Borwein algorithm for (l_0) regularized optimization (2020)
- Mousavi, Ahmad; Rezaee, Mehdi; Ayanzadeh, Ramin: A survey on compressive sensing: classical results and recent advancements (2020)
- Shen, Chungen; Xue, Wenjuan; Zhang, Lei-Hong; Wang, Baiyun: An active-set proximal-Newton algorithm for (\ell_1) regularized optimization problems with box constraints (2020)
- Wang, Guoqiang; Wei, Xinyuan; Yu, Bo; Xu, Lijun: An efficient proximal block coordinate homotopy method for large-scale sparse least squares problems (2020)
- Zhang, Chao; Chen, Xiaojun: A smoothing active set method for linearly constrained non-Lipschitz nonconvex optimization (2020)
- Azmi, Behzad; Kunisch, Karl: A hybrid finite-dimensional RHC for stabilization of time-varying parabolic equations (2019)
- Becker, Stephen; Fadili, Jalal; Ochs, Peter: On quasi-Newton forward-backward splitting: proximal calculus and convergence (2019)
- Cheng, Wanyou; Hu, Qingjie; Li, Donghui: A fast conjugate gradient algorithm with active set prediction for (\ell_1) optimization (2019)
- Esmaeili, Hamid; Shabani, Shima; Kimiaei, Morteza: A new generalized shrinkage conjugate gradient method for sparse recovery (2019)
- Lin, Meixia; Liu, Yong-Jin; Sun, Defeng; Toh, Kim-Chuan: Efficient sparse semismooth Newton methods for the clustered Lasso problem (2019)
- Nutini, Julie; Schmidt, Mark; Hare, Warren: “Active-set complexity” of proximal gradient: how long does it take to find the sparsity pattern? (2019)
- Yang, Tianbao; Zhang, Lijun; Jin, Rong; Zhu, Shenghuo; Zhou, Zhi-Hua: A simple homotopy proximal mapping algorithm for compressive sensing (2019)
- Zhao, Huan; Gao, Zhenghong; Xu, Fang; Zhang, Yidian; Huang, Jiangtao: An efficient adaptive forward-backward selection method for sparse polynomial chaos expansion (2019)