The following pages link to Decoding by Linear Programming (Q3546644):
Displaying 50 items.
- Adaptive iterative hard thresholding for least absolute deviation problems with sparsity constraints (Q2108537) (← links)
- Local recovery bounds for prior support constrained compressed sensing (Q2113400) (← links)
- Weighted \(\ell_p\) (\(0<p\le 1\)) minimization with non-uniform weights for sparse recovery under partial support information (Q2115047) (← links)
- Sparse PSD approximation of the PSD cone (Q2118107) (← links)
- Sparse representation of vectors in lattices and semigroups (Q2118144) (← links)
- Fundamental barriers to high-dimensional regression with convex penalties (Q2119224) (← links)
- Low-rank matrix recovery with Ky Fan 2-\(k\)-norm (Q2124796) (← links)
- Exploiting prior knowledge in compressed sensing to design robust systems for endoscopy image recovery (Q2125346) (← links)
- Sparse solutions to an underdetermined system of linear equations via penalized Huber loss (Q2129205) (← links)
- Sparse high-dimensional linear regression. Estimating squared error and a phase transition (Q2131259) (← links)
- Wigner and Wishart ensembles for sparse Vinberg models (Q2135512) (← links)
- De-biasing the Lasso with degrees-of-freedom adjustment (Q2136990) (← links)
- On the optimization landscape of tensor decompositions (Q2144549) (← links)
- Iteratively reweighted least squares and slime mold dynamics: connection and convergence (Q2149565) (← links)
- Compressive sensing of high betweenness centrality nodes in networks (Q2150293) (← links)
- Sparse signal recovery via generalized Gaussian function (Q2154451) (← links)
- The restricted isometry property of block diagonal matrices for group-sparse signal recovery (Q2155809) (← links)
- A new sufficient condition for sparse recovery with multiple orthogonal least squares (Q2157863) (← links)
- Screening for a reweighted penalized conditional gradient method (Q2165597) (← links)
- Gradient projection Newton pursuit for sparsity constrained optimization (Q2168680) (← links)
- The springback penalty for robust signal recovery (Q2168687) (← links)
- Generalizing CoSaMP to signals from a union of low dimensional linear subspaces (Q2175016) (← links)
- A hybrid quasi-Newton projected-gradient method with application to lasso and basis-pursuit denoising (Q2175442) (← links)
- Smoothing inertial projection neural network for minimization \(L_{p-q}\) in sparse signal reconstruction (Q2179318) (← links)
- Parametrized quasi-soft thresholding operator for compressed sensing and matrix completion (Q2185044) (← links)
- An improved total variation regularized RPCA for moving object detection with dynamic background (Q2190301) (← links)
- Sparse signal reconstruction via the approximations of \(\ell_0\) quasinorm (Q2190319) (← links)
- Iterative hard thresholding for compressed data separation (Q2190470) (← links)
- A smoothing method for sparse optimization over convex sets (Q2191281) (← links)
- A filtered bucket-clustering method for projection onto the simplex and the \(\ell_1\) ball (Q2191778) (← links)
- Subspace learning by \(\ell^0\)-induced sparsity (Q2193787) (← links)
- Flexible semi-supervised embedding based on adaptive loss regression: application to image categorization (Q2195309) (← links)
- Deterministic construction of compressed sensing matrices from constant dimension codes (Q2197399) (← links)
- Group sparse recovery in impulsive noise via alternating direction method of multipliers (Q2197950) (← links)
- A perturbation analysis of nonconvex block-sparse compressed sensing (Q2198516) (← links)
- Effective zero-norm minimization algorithms for noisy compressed sensing (Q2198627) (← links)
- Geological facies recovery based on weighted \(\ell_1\)-regularization (Q2198925) (← links)
- Three deterministic constructions of compressed sensing matrices with low coherence (Q2202898) (← links)
- Deletion correcting codes meet the Littlewood-Offord problem (Q2205891) (← links)
- Numerical aspects for approximating governing equations using data (Q2214649) (← links)
- A general framework for Bayes structured linear models (Q2215762) (← links)
- Level set methods for stochastic discontinuity detection in nonlinear problems (Q2221452) (← links)
- New preconditioners applied to linear programming and the compressive sensing problems (Q2226494) (← links)
- Phaseless compressive sensing using partial support information (Q2228381) (← links)
- Discrete optimization methods for group model selection in compressed sensing (Q2235146) (← links)
- Convergence and stability analysis of iteratively reweighted least squares for noisy block sparse recovery (Q2238869) (← links)
- A two-step iterative algorithm for sparse hyperspectral unmixing via total variation (Q2242712) (← links)
- The nonnegative zero-norm minimization under generalized \(Z\)-matrix measurement (Q2251573) (← links)
- Sharp RIP bound for sparse signal and low-rank matrix recovery (Q2252129) (← links)
- A new iterative firm-thresholding algorithm for inverse problems with sparsity constraints (Q2252134) (← links)
