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 31 articles , 1 standard article )
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- Treister, Eran; Turek, Javier S.; Yavneh, Irad: A multilevel framework for sparse optimization with application to inverse covariance estimation and logistic regression (2016)
- Huang, Yakui; Liu, Hongwei: A Barzilai-Borwein type method for minimizing composite functions (2015)
- Lin, Qihang; Xiao, Lin: An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization (2015)
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- Aybat, N.S.; Iyengar, G.: A unified approach for minimizing composite norms (2014)
- Cao, Shuhan; Xiao, Yunhai; Zhu, Hong: Linearized alternating directions method for $\ell_1$-norm inequality constrained $\ell_1$-norm minimization (2014)
- Fountoulakis, Kimon; Gondzio, Jacek; Zhlobich, Pavel: Matrix-free interior point method for compressed sensing problems (2014)
- Lee, Jason D.; Sun, Yuekai; Saunders, Michael A.: Proximal Newton-type methods for minimizing composite functions (2014)
- Li, Yingying; Osher, Stanley; Tsai, Richard: Heat source identification based on $\ell_1$ constrained minimization (2014)
- Porcelli, Margherita; Rinaldi, Francesco: A variable fixing version of the two-block nonlinear constrained Gauss-Seidel algorithm for $\ell_1$-regularized least-squares (2014)
- Wang, Zhaoran; Liu, Han; Zhang, Tong: Optimal computational and statistical rates of convergence for sparse nonconvex learning problems (2014)
- Xiao, Yunhai; Wu, Soon-Yi; Qi, Liqun: Nonmonotone Barzilai-Borwein gradient algorithm for $\ell_1$-regularized nonsmooth minimization in compressive sensing (2014)
- Gu, Ming; Lim, Lek-Heng; Wu, Cinna Julie: ParNes: A rapidly convergent algorithm for accurate recovery of sparse and approximately sparse signals (2013)
- Setzer, Simon; Steidl, Gabriele; Morgenthaler, Jan: A cyclic projected gradient method (2013)
- Xiao, Lin; Zhang, Tong: A proximal-gradient homotopy method for the sparse least-squares problem (2013)
- Aybat, N.S.; Iyengar, G.: A first-order augmented Lagrangian method for compressed sensing (2012)