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.
- Stable optimizationless recovery from phaseless linear measurements (Q485222) (← links)
- Optimal rank-sparsity decomposition (Q486401) (← links)
- Equivalence and strong equivalence between the sparsest and least \(\ell _1\)-norm nonnegative solutions of linear systems and their applications (Q489110) (← links)
- Conditional gradient algorithms for norm-regularized smooth convex optimization (Q494314) (← links)
- Extreme point inequalities and geometry of the rank sparsity ball (Q494341) (← links)
- A partial proximal point algorithm for nuclear norm regularized matrix least squares problems (Q495943) (← links)
- Decomposable norm minimization with proximal-gradient homotopy algorithm (Q513723) (← links)
- Dimensionality reduction with subgaussian matrices: a unified theory (Q515989) (← links)
- Finding a low-rank basis in a matrix subspace (Q517309) (← links)
- Convergence of fixed-point continuation algorithms for matrix rank minimization (Q535287) (← links)
- Fixed point and Bregman iterative methods for matrix rank minimization (Q543413) (← links)
- Estimation of high-dimensional low-rank matrices (Q548539) (← links)
- Estimation of (near) low-rank matrices with noise and high-dimensional scaling (Q548547) (← links)
- Optimal selection of reduced rank estimators of high-dimensional matrices (Q548562) (← links)
- Approximation accuracy, gradient methods, and error bound for structured convex optimization (Q607498) (← links)
- Null space conditions and thresholds for rank minimization (Q633114) (← links)
- Solving optimization problems on ranks and inertias of some constrained nonlinear matrix functions via an algebraic linearization method (Q651143) (← links)
- Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion (Q661157) (← links)
- Convex optimization methods for dimension reduction and coefficient estimation in multivariate linear regression (Q662292) (← links)
- Low-rank matrix recovery via rank one tight frame measurements (Q666654) (← links)
- Analysis of convergence for the alternating direction method applied to joint sparse recovery (Q668704) (← links)
- A new algorithm for positive semidefinite matrix completion (Q670219) (← links)
- Max-norm optimization for robust matrix recovery (Q681486) (← links)
- Interpreting latent variables in factor models via convex optimization (Q681494) (← links)
- An alternating direction algorithm for matrix completion with nonnegative factors (Q693195) (← links)
- Nuclear norm minimization for the planted clique and biclique problems (Q717132) (← links)
- Explicit frames for deterministic phase retrieval via PhaseLift (Q723007) (← links)
- Sparse functional identification of complex cells from spike times and the decoding of visual stimuli (Q723690) (← links)
- Characterization of the equivalence of robustification and regularization in linear and matrix regression (Q723995) (← links)
- Robust recovery of complex exponential signals from random Gaussian projections via low rank Hankel matrix reconstruction (Q739472) (← links)
- A simple prior-free method for non-rigid structure-from-motion factorization (Q740406) (← links)
- Learning non-parametric basis independent models from point queries via low-rank methods (Q741260) (← links)
- Fast global convergence of gradient methods for high-dimensional statistical recovery (Q741793) (← links)
- Generalized ADMM with optimal indefinite proximal term for linearly constrained convex optimization (Q781096) (← links)
- A shrinkage principle for heavy-tailed data: high-dimensional robust low-rank matrix recovery (Q820791) (← links)
- Sparse recovery via nonconvex regularized \(M\)-estimators over \(\ell_q\)-balls (Q830557) (← links)
- Synthesizing invariant barrier certificates via difference-of-convex programming (Q832194) (← links)
- Semidefinite programming and sums of Hermitian squares of noncommutative polynomials (Q847674) (← links)
- Learning Markov random walks for robust subspace clustering and estimation (Q889299) (← links)
- A proximal alternating linearization method for minimizing the sum of two convex functions (Q892779) (← links)
- Rank constrained matrix best approximation problem (Q894411) (← links)
- Guaranteed recovery of planted cliques and dense subgraphs by convex relaxation (Q896191) (← links)
- From compression to compressed sensing (Q905909) (← links)
- A perturbation inequality for concave functions of singular values and its applications in low-rank matrix recovery (Q905912) (← links)
- Finding the strongly rank-minimizing solution to the linear matrix inequality (Q927546) (← links)
- A general family of trimmed estimators for robust high-dimensional data analysis (Q1616324) (← links)
- A penalty method for rank minimization problems in symmetric matrices (Q1616933) (← links)
- Low-rank parameterization of planar domains for isogeometric analysis (Q1647777) (← links)
- Trace regression model with simultaneously low rank and row(column) sparse parameter (Q1658399) (← links)
- Stable analysis of compressive principal component pursuit (Q1662624) (← links)
