The following pages link to Decoding by Linear Programming (Q3546644):
Displaying 50 items.
- A reweighted nuclear norm minimization algorithm for low rank matrix recovery (Q2252420) (← links)
- Stable restoration and separation of approximately sparse signals (Q2252500) (← links)
- Fast thresholding algorithms with feedbacks for sparse signal recovery (Q2252504) (← links)
- Sparse recovery with coherent tight frames via analysis Dantzig selector and analysis LASSO (Q2252508) (← links)
- Sparse signals recovery from noisy measurements by orthogonal matching pursuit (Q2254767) (← links)
- Every Hilbert space frame has a Naimark complement (Q2257548) (← links)
- Sparse trace norm regularization (Q2259743) (← links)
- Necessary and sufficient conditions of solution uniqueness in 1-norm minimization (Q2260650) (← links)
- Sparse-view ultrasound diffraction tomography using compressed sensing with nonuniform FFT (Q2262459) (← links)
- Cirrhosis classification based on texture classification of random features (Q2262534) (← links)
- A numerical exploration of compressed sampling recovery (Q2267399) (← links)
- Stability and instance optimality for Gaussian measurements in compressed sensing (Q2269902) (← links)
- Construction of highly redundant incoherent unit norm tight frames as a union of orthonormal bases (Q2274406) (← links)
- The convergence guarantee of the iterative hard thresholding algorithm with suboptimal feedbacks for large systems (Q2275173) (← links)
- On convex envelopes and regularization of non-convex functionals without moving global minima (Q2275270) (← links)
- On the computation of sparse solutions to the controllability problem for discrete-time linear systems (Q2275285) (← links)
- On a monotone scheme for nonconvex nonsmooth optimization with applications to fracture mechanics (Q2275329) (← links)
- On rank awareness, thresholding, and MUSIC for joint sparse recovery (Q2278470) (← links)
- Signal reconstruction by conjugate gradient algorithm based on smoothing \(l_1\)-norm (Q2279178) (← links)
- Distributed secure state estimation for cyber-physical systems under sensor attacks (Q2280882) (← links)
- Recovering low-rank and sparse matrix based on the truncated nuclear norm (Q2281698) (← links)
- A survey of randomized algorithms for training neural networks (Q2282875) (← links)
- Sorted concave penalized regression (Q2284364) (← links)
- An effective algorithm for the spark of sparse binary measurement matrices (Q2287709) (← links)
- On the differences between \(L_2\) boosting and the Lasso (Q2288790) (← links)
- A compressive sensing based privacy preserving outsourcing of image storage and identity authentication service in cloud (Q2293031) (← links)
- An efficient privacy-preserving compressive data gathering scheme in WSNs (Q2293079) (← links)
- An accelerated version of alternating direction method of multipliers for TV minimization in EIT (Q2293764) (← links)
- On sparse beamformer design with reverberation (Q2295315) (← links)
- A new smoothed L0 regularization approach for sparse signal recovery (Q2298142) (← links)
- A new sparse recovery method for the inverse acoustic scattering problem (Q2300540) (← links)
- Erasure recovery matrices for encoder protection (Q2300758) (← links)
- RIP-based performance guarantee for low-tubal-rank tensor recovery (Q2306402) (← links)
- On privacy preserving data release of linear dynamic networks (Q2307532) (← links)
- Accelerated iterative hard thresholding algorithm for \(l_0\) regularized regression problem (Q2307753) (← links)
- Compressed dictionary learning (Q2310825) (← links)
- The sparsest solution of the union of finite polytopes via its nonconvex relaxation (Q2311128) (← links)
- Deterministic constructions of compressed sensing matrices based on codes (Q2311429) (← links)
- New conditions on stable recovery of weighted sparse signals via weighted \(l_1\) minimization (Q2312513) (← links)
- On the strong restricted isometry property of Bernoulli random matrices (Q2315024) (← links)
- Truncated sparse approximation property and truncated \(q\)-norm minimization (Q2322801) (← links)
- Alternating direction and Taylor expansion minimization algorithms for unconstrained nuclear norm optimization (Q2322836) (← links)
- Compressed sensing MR image reconstruction exploiting TGV and wavelet sparsity (Q2330192) (← links)
- Optimal RIP bounds for sparse signals recovery via \(\ell_p\) minimization (Q2330928) (← links)
- Sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit (Q2330942) (← links)
- A modified primal-dual method with applications to some sparse recovery problems (Q2335126) (← links)
- Outlier deletion based improvement on the stomp algorithm for sparse solution of large-scale underdetermined problems (Q2335937) (← links)
- Weaker regularity conditions and sparse recovery in high-dimensional regression (Q2336858) (← links)
- A smoothed \(l_0\)-norm and \(l_1\)-norm regularization algorithm for computed tomography (Q2337028) (← links)
- Preconditioning for orthogonal matching pursuit with noisy and random measurements: the Gaussian case (Q2338338) (← links)
