Pages that link to "Item:Q984656"
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The following pages link to Sparse recovery by non-convex optimization - instance optimality (Q984656):
Displaying 37 items.
- Nomonotone spectral gradient method for sparse recovery (Q256063) (← links)
- Recovery of sparsest signals via \(\ell^q \)-minimization (Q413642) (← links)
- Perturbations of measurement matrices and dictionaries in compressed sensing (Q442524) (← links)
- Robustness of orthogonal matching pursuit under restricted isometry property (Q476748) (← links)
- Analysis of orthogonal multi-matching pursuit under restricted isometry property (Q477291) (← links)
- Optimal computational and statistical rates of convergence for sparse nonconvex learning problems (Q482875) (← links)
- On the null space property of \(l_q\)-minimization for \(0 < q \leq 1\) in compressed sensing (Q492558) (← links)
- Restricted \(p\)-isometry property and its application for nonconvex compressive sensing (Q695638) (← links)
- On support sizes of restricted isometry constants (Q711053) (← links)
- Instance-optimality in probability with an \(\ell _1\)-minimization decoder (Q734324) (← links)
- Sparse recovery in probability via \(l_q\)-minimization with Weibull random matrices for \(0 < q\leq 1\) (Q1747365) (← links)
- Linear program relaxation of sparse nonnegative recovery in compressive sensing microarrays (Q1929588) (← links)
- Restricted \(p\)-isometry properties of partially sparse signal recovery (Q1956098) (← links)
- Compressed sensing of color images (Q1957911) (← links)
- A sharp RIP condition for orthogonal matching pursuit (Q2015581) (← links)
- A unified primal dual active set algorithm for nonconvex sparse recovery (Q2038299) (← links)
- A class of null space conditions for sparse recovery via nonconvex, non-separable minimizations (Q2211058) (← links)
- A reweighted nuclear norm minimization algorithm for low rank matrix recovery (Q2252420) (← links)
- Optimal RIP bounds for sparse signals recovery via \(\ell_p\) minimization (Q2330928) (← links)
- Optimal \(D\)-RIP bounds in compressed sensing (Q2353238) (← links)
- Convergence and stability of iteratively reweighted least squares for low-rank matrix recovery (Q2360780) (← links)
- Quantization of compressive samples with stable and robust recovery (Q2409038) (← links)
- Generalized sparse recovery model and its neural dynamical optimization method for compressed sensing (Q2411691) (← links)
- Nonconvex sorted \(\ell_1\) minimization for sparse approximation (Q2516375) (← links)
- A necessary and sufficient condition for sparse vector recovery via \(\ell_1-\ell_2\) minimization (Q2667049) (← links)
- Noise-Shaping Quantization Methods for Frame-Based and Compressive Sampling Systems (Q2799920) (← links)
- Exact low-rank matrix recovery via nonconvex Schatten \(p\)-minimization (Q2846490) (← links)
- Stability of lq-analysis based dual frame with Weibull matrices for 0 < q ≤ 1 (Q2958494) (← links)
- CURVELET-WAVELET REGULARIZED SPLIT BREGMAN ITERATION FOR COMPRESSED SENSING (Q3084700) (← links)
- Sparse signal recovery via non-convex optimization and overcomplete dictionaries (Q4554319) (← links)
- Perfect Recovery Conditions for Non-negative Sparse Modeling (Q4620510) (← links)
- Least Sparsity of $p$-Norm Based Optimization Problems with $p>1$ (Q4687239) (← links)
- A new sufficient condition for sparse vector recovery via ℓ1 − ℓ2 local minimization (Q5016825) (← links)
- (Q5214255) (← links)
- The sampling complexity on nonconvex sparse phase retrieval problem (Q6085637) (← links)
- Accelerated sparse recovery via gradient descent with nonlinear conjugate gradient momentum (Q6101532) (← links)
- Phase transition and higher order analysis of \(L_q\) regularization under dependence (Q6663352) (← links)
