Optimal $k$-Thresholding Algorithms for Sparse Optimization Problems
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Publication:5210512
DOI10.1137/18M1219187zbMath1435.90113arXiv1909.00717MaRDI QIDQ5210512
Publication date: 21 January 2020
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1909.00717
convex optimizationiterative algorithmsrestricted isometry propertysparse optimizationhard thresholdingoptimal \(k\)-thresholding
Convex programming (90C25) Nonlinear programming (90C30) Linear programming (90C05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Inverse problems in linear algebra (15A29) Iterative numerical methods for linear systems (65F10)
Related Items (8)
Improved RIP-based bounds for guaranteed performance of two compressed sensing algorithms ⋮ Newton-type optimal thresholding algorithms for sparse optimization problems ⋮ Heuristics for Finding Sparse Solutions of Linear Inequalities ⋮ Heavy-ball-based optimal thresholding algorithms for sparse linear inverse problems ⋮ Heavy-ball-based hard thresholding algorithms for sparse signal recovery ⋮ Scaled proximal gradient methods for sparse optimization problems ⋮ Dual-density-based reweighted \(\ell_1\)-algorithms for a class of \(\ell_0\)-minimization problems ⋮ Partial gradient optimal thresholding algorithms for a class of sparse optimization problems
Uses Software
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