Pages that link to "Item:Q3586174"
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The following pages link to Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization (Q3586174):
Displaying 50 items.
- Learning latent variable Gaussian graphical model for biomolecular network with low sample complexity (Q2011725) (← links)
- Online optimization for max-norm regularization (Q2014582) (← links)
- On recovery guarantees for one-bit compressed sensing on manifolds (Q2022611) (← links)
- Estimation of the parameters of a weighted nuclear norm model and its application in image denoising (Q2023235) (← links)
- Riemannian gradient descent methods for graph-regularized matrix completion (Q2029849) (← links)
- Quartic first-order methods for low-rank minimization (Q2031993) (← links)
- Low-rank matrix completion in a general non-orthogonal basis (Q2032243) (← links)
- Phase retrieval with PhaseLift algorithm (Q2033498) (← links)
- Low-rank matrix recovery via regularized nuclear norm minimization (Q2036488) (← links)
- Oracle posterior contraction rates under hierarchical priors (Q2044331) (← links)
- Double fused Lasso regularized regression with both matrix and vector valued predictors (Q2044365) (← links)
- Regularization parameter selection for the low rank matrix recovery (Q2046538) (← links)
- Tensor theta norms and low rank recovery (Q2048814) (← links)
- Error bound of critical points and KL property of exponent 1/2 for squared F-norm regularized factorization (Q2052408) (← links)
- Tensor-free proximal methods for lifted bilinear/quadratic inverse problems with applications to phase retrieval (Q2052715) (← links)
- A new method based on the manifold-alternative approximating for low-rank matrix completion (Q2061479) (← links)
- Low-rank dynamic mode decomposition: an exact and tractable solution (Q2062877) (← links)
- Sampling from non-smooth distributions through Langevin diffusion (Q2065460) (← links)
- Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence (Q2067681) (← links)
- An adaptation for iterative structured matrix completion (Q2072669) (← links)
- Efficient proximal mapping computation for low-rank inducing norms (Q2073049) (← links)
- Low phase-rank approximation (Q2074973) (← links)
- An inexact symmetric ADMM algorithm with indefinite proximal term for sparse signal recovery and image restoration problems (Q2088791) (← links)
- On the robustness of minimum norm interpolators and regularized empirical risk minimizers (Q2091842) (← links)
- Riemannian conjugate gradient descent method for fixed multi rank third-order tensor completion (Q2095166) (← links)
- A semismooth Newton-based augmented Lagrangian algorithm for density matrix least squares problems (Q2095559) (← links)
- Tensor completion via a generalized transformed tensor t-product decomposition without t-SVD (Q2103412) (← links)
- An ensemble of high rank matrices arising from tournaments (Q2104983) (← links)
- Encoding inductive invariants as barrier certificates: synthesis via difference-of-convex programming (Q2105455) (← links)
- New challenges in covariance estimation: multiple structures and coarse quantization (Q2106471) (← links)
- Efficient low-rank regularization-based algorithms combining advanced techniques for solving tensor completion problems with application to color image recovering (Q2112681) (← links)
- New and explicit constructions of unbalanced Ramanujan bipartite graphs (Q2115273) (← links)
- Low tubal rank tensor recovery using the Bürer-Monteiro factorisation approach. Application to optical coherence tomography (Q2122021) (← links)
- Low-rank matrix recovery with Ky Fan 2-\(k\)-norm (Q2124796) (← links)
- Poisson reduced-rank models with sparse loadings (Q2132046) (← links)
- Learning with tree tensor networks: complexity estimates and model selection (Q2137001) (← links)
- Regularized high dimension low tubal-rank tensor regression (Q2137811) (← links)
- A fast proximal iteratively reweighted nuclear norm algorithm for nonconvex low-rank matrix minimization problems (Q2143100) (← links)
- Noisy tensor completion via the sum-of-squares hierarchy (Q2144539) (← links)
- Fitting Laplacian regularized stratified Gaussian models (Q2147926) (← links)
- An inexact proximal DC algorithm with sieving strategy for rank constrained least squares semidefinite programming (Q2148144) (← links)
- Bias versus non-convexity in compressed sensing (Q2155168) (← links)
- Non-convex low-rank representation combined with rank-one matrix sum for subspace clustering (Q2156581) (← links)
- Augmented Lagrangian methods for convex matrix optimization problems (Q2158112) (← links)
- Kurdyka-Łojasiewicz exponent via inf-projection (Q2162122) (← links)
- Inertial alternating direction method of multipliers for non-convex non-smooth optimization (Q2162531) (← links)
- Enhanced alternating energy minimization methods for stochastic Galerkin matrix equations (Q2162725) (← links)
- Trading off \(1\)-norm and sparsity against rank for linear models using mathematical optimization: \(1\)-norm minimizing partially reflexive ah-symmetric generalized inverses (Q2165590) (← links)
- Extended randomized Kaczmarz method for sparse least squares and impulsive noise problems (Q2168919) (← links)
- Low rank matrix recovery with impulsive noise (Q2171174) (← links)
