Pages that link to "Item:Q2840384"

The following pages link to Improved iteratively reweighted least squares for unconstrained smoothed \(\ell_q\) minimization (Q2840384):

Displaying 50 items.

• Robust sparse recovery via a novel convex model (Q2079105) (← links)
• The \(\ell_{2,p}\) regularized total variation with overlapping group sparsity prior for image restoration with impulse noise (Q2098805) (← links)
• Extrapolated smoothing descent algorithm for constrained nonconvex and nonsmooth composite problems (Q2105897) (← links)
• A smoothing proximal gradient algorithm for matrix rank minimization problem (Q2114821) (← links)
• An interior stochastic gradient method for a class of non-Lipschitz optimization problems (Q2161545) (← links)
• Gradient projection Newton pursuit for sparsity constrained optimization (Q2168680) (← links)
• The springback penalty for robust signal recovery (Q2168687) (← links)
• A nonconvex truncated regularization and box-constrained model for CT reconstruction (Q2198001) (← links)
• Online Schatten quasi-norm minimization for robust principal component analysis (Q2201647) (← links)
• A multi-stage convex relaxation approach to noisy structured low-rank matrix recovery (Q2220914) (← links)
• Convergence and stability analysis of iteratively reweighted least squares for noisy block sparse recovery (Q2238869) (← links)
• An efficient non-convex total variation approach for image deblurring and denoising (Q2242083) (← links)
• A reweighted nuclear norm minimization algorithm for low rank matrix recovery (Q2252420) (← links)
• Iterative re-weighted least squares algorithm for \(l_p\)-minimization with tight frame and \(0 < p \leq 1\) (Q2273889) (← links)
• A gradient descent based algorithm for \(\ell_p\) minimization (Q2286916) (← links)
• RIP-based performance guarantee for low-tubal-rank tensor recovery (Q2306402) (← links)
• Optimal RIP bounds for sparse signals recovery via \(\ell_p\) minimization (Q2330928) (← links)
• A non-convex algorithm framework based on DC programming and DCA for matrix completion (Q2340366) (← links)
• Convergence and stability of iteratively reweighted least squares for low-rank matrix recovery (Q2360780) (← links)
• \(S_{1/2}\) regularization methods and fixed point algorithms for affine rank minimization problems (Q2364127) (← links)
• Generalized sparse recovery model and its neural dynamical optimization method for compressed sensing (Q2411691) (← links)
• A singular value \(p\)-shrinkage thresholding algorithm for low rank matrix recovery (Q2419553) (← links)
• Convergence analysis of projected gradient descent for Schatten-\(p\) nonconvex matrix recovery (Q2515314) (← links)
• Nonconvex sorted \(\ell_1\) minimization for sparse approximation (Q2516375) (← links)
• On the Schatten \(p\)-quasi-norm minimization for low-rank matrix recovery (Q2659733) (← links)
• Smoothing strategy along with conjugate gradient algorithm for signal reconstruction (Q2660684) (← links)
• Multi-competitive viruses over time-varying networks with mutations and human awareness (Q2662294) (← links)
• Newton method for \(\ell_0\)-regularized optimization (Q2665935) (← links)
• Proximal linearization methods for Schatten \(p\)-quasi-norm minimization (Q2678970) (← links)
• A new hybrid \(l_p\)-\(l_2\) model for sparse solutions with applications to image processing (Q2691208) (← links)
• Low rank matrix minimization with a truncated difference of nuclear norm and Frobenius norm regularization (Q2691258) (← links)
• Calmness of partial perturbation to composite rank constraint systems and its applications (Q2694518) (← links)
• A global exact penalty for rank-constrained optimization problem and applications (Q2696914) (← links)
• DC Approximation Approach for ℓ0-minimization in Compressed Sensing (Q2808067) (← links)
• Penalty decomposition methods for rank minimization (Q2943834) (← links)
• A nonconvex approach to low-rank matrix completion using convex optimization (Q2955982) (← links)
• Sparse Approximation using $\ell_1-\ell_2$ Minimization and Its Application to Stochastic Collocation (Q2964447) (← links)
• Truncated $l_{1-2}$ Models for Sparse Recovery and Rank Minimization (Q3130749) (← links)
• Sparse signal recovery by accelerated ℓ_q (0&lt;q&lt;1) thresholding algorithm (Q3177472) (← links)
• Several Classes of Stationary Points for Rank Regularized Minimization Problems (Q3300765) (← links)
• Iteratively reweighted least squares minimization for sparse recovery (Q3655588) (← links)
• A Nonmonotone Alternating Updating Method for a Class of Matrix Factorization Problems (Q4562251) (← links)
• A null-space-based weighted<i>l</i>₁minimization approach to compressed sensing (Q4603732) (← links)
• Sparse Solutions by a Quadratically Constrained ℓq (0 &lt;<i>q</i>&lt; 1) Minimization Model (Q4995085) (← links)
• On a general smoothly truncated regularization for variational piecewise constant image restoration: construction and convergent algorithms (Q5000580) (← links)
• <b> <i>ℓ</i> ₁ − <i>αℓ</i> ₂ </b> minimization methods for signal and image reconstruction with impulsive noise removal (Q5000605) (← links)
• The Dantzig selector: recovery of signal via ℓ ₁ − αℓ ₂ minimization (Q5014489) (← links)
• Isotropic non-Lipschitz regularization for sparse representations of random fields on the sphere (Q5018371) (← links)
• An accelerated majorization-minimization algorithm with convergence guarantee for non-Lipschitz wavelet synthesis model ^* (Q5019920) (← links)
• Quadratic Convergence of Smoothing Newton's Method for 0/1 Loss Optimization (Q5020852) (← links)