The following pages link to Decoding by Linear Programming (Q3546644):
Displaying 50 items.
- Deterministic constructions of compressed sensing matrices based on optimal codebooks and codes (Q2008183) (← links)
- On the convergence of the iterates of proximal gradient algorithm with extrapolation for convex nonsmooth minimization problems (Q2010091) (← links)
- A sharp RIP condition for orthogonal matching pursuit (Q2015581) (← links)
- An augmented Lagrangian algorithm for total bounded variation regularization based image deblurring (Q2017238) (← links)
- Complexity and applications of the homotopy principle for uniformly constrained sparse minimization (Q2019907) (← links)
- Greedy variance estimation for the LASSO (Q2019914) (← links)
- Consistency bounds and support recovery of d-stationary solutions of sparse sample average approximations (Q2022171) (← links)
- Linear convergence of inexact descent method and inexact proximal gradient algorithms for lower-order regularization problems (Q2022292) (← links)
- Sparse space-time models: concentration inequalities and Lasso (Q2028941) (← links)
- Phase retrieval with PhaseLift algorithm (Q2033498) (← links)
- Sparse recovery in bounded Riesz systems with applications to numerical methods for PDEs (Q2036421) (← links)
- Application of ESN prediction model based on compressed sensing in stock market (Q2038120) (← links)
- A unified primal dual active set algorithm for nonconvex sparse recovery (Q2038299) (← links)
- MRI simulation-based evaluation of an efficient under-sampling approach (Q2038799) (← links)
- Data-driven algorithm selection and tuning in optimization and signal processing (Q2043447) (← links)
- Oracle posterior contraction rates under hierarchical priors (Q2044331) (← links)
- Level-set subdifferential error bounds and linear convergence of Bregman proximal gradient method (Q2046546) (← links)
- Dual-density-based reweighted \(\ell_1\)-algorithms for a class of \(\ell_0\)-minimization problems (Q2052391) (← links)
- The distribution of the Lasso: uniform control over sparse balls and adaptive parameter tuning (Q2054498) (← links)
- Optimal portfolio selections via \(\ell_{1, 2}\)-norm regularization (Q2057226) (← links)
- Nonuniqueness of solutions of a class of \(\ell_0\)-minimization problems (Q2059197) (← links)
- A sparse optimization problem with hybrid \(L_2\)-\(L_p\) regularization for application of magnetic resonance brain images (Q2060052) (← links)
- On the computational complexity of the secure state-reconstruction problem (Q2063856) (← links)
- The vulnerability of distributed state estimator under stealthy attacks (Q2065222) (← links)
- Efficiency of orthogonal super greedy algorithm under the restricted isometry property (Q2067856) (← links)
- An inexact proximal gradient algorithm with extrapolation for a class of nonconvex nonsmooth optimization problems (Q2067857) (← links)
- Smoothing Newton method for \(\ell^0\)-\(\ell^2\) regularized linear inverse problem (Q2072164) (← links)
- An adaptation for iterative structured matrix completion (Q2072669) (← links)
- Analysis of generalized Bregman surrogate algorithms for nonsmooth nonconvex statistical learning (Q2073715) (← links)
- Regret lower bound and optimal algorithm for high-dimensional contextual linear bandit (Q2074307) (← links)
- Structured iterative hard thresholding with on- and off-grid applications (Q2074952) (← links)
- Asymptotic analysis for extreme eigenvalues of principal minors of random matrices (Q2075335) (← links)
- Flexible construction of measurement matrices in compressed sensing based on extensions of incidence matrices of combinatorial designs (Q2078717) (← links)
- The all-or-nothing phenomenon in sparse linear regression (Q2078961) (← links)
- Robust sparse recovery via a novel convex model (Q2079105) (← links)
- Partial gradient optimal thresholding algorithms for a class of sparse optimization problems (Q2079693) (← links)
- Sufficient conditions for the uniqueness of solution of the weighted norm minimization problem (Q2079999) (← links)
- Perturbation analysis of \(L_{1-2}\) method for robust sparse recovery (Q2082139) (← links)
- Weighted thresholding homotopy method for sparsity constrained optimization (Q2082209) (← links)
- Learning ``best'' kernels from data in Gaussian process regression. With application to aerodynamics (Q2083686) (← links)
- Commonsense explanations of sparsity, Zipf law, and Nash's bargaining solution (Q2086143) (← links)
- Nonregular and minimax estimation of individualized thresholds in high dimension with binary responses (Q2091840) (← links)
- On the grouping effect of the \(l_{1-2}\) models (Q2093808) (← links)
- A convex relaxation framework consisting of a primal-dual alternative algorithm for solving \(\ell_0\) sparsity-induced optimization problems with application to signal recovery based image restoration (Q2095175) (← links)
- Search for sparse solutions of super-large systems with a tensor structure (Q2101411) (← links)
- A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks (Q2103975) (← links)
- Gradient projection Newton algorithm for sparse collaborative learning using synthetic and real datasets of applications (Q2104053) (← links)
- Hierarchical compressed sensing (Q2106467) (← links)
- Proof methods for robust low-rank matrix recovery (Q2106469) (← links)
- Unbiasing in iterative reconstruction algorithms for discrete compressed sensing (Q2106477) (← links)
