The following pages link to Decoding by Linear Programming (Q3546644):
Displaying 50 items.
- Recovery of signals under the condition on RIC and ROC via prior support information (Q1713645) (← links)
- Sparse Markowitz portfolio selection by using stochastic linear complementarity approach (Q1716964) (← links)
- Linear convergence of the randomized sparse Kaczmarz method (Q1717238) (← links)
- Image encryption technique combining compressive sensing with double random-phase encoding (Q1721294) (← links)
- A simpler approach to coefficient regularized support vector machines regression (Q1722337) (← links)
- Signal recovery under mutual incoherence property and oracle inequalities (Q1731904) (← links)
- Recovery analysis for weighted mixed \(\ell_2 / \ell_p\) minimization with \(0 < p \leq 1\) (Q1736367) (← links)
- An overview on the applications of matrix theory in wireless communications and signal processing (Q1736835) (← links)
- A remark on joint sparse recovery with OMP algorithm under restricted isometry property (Q1740232) (← links)
- Sparse signal recovery with prior information by iterative reweighted least squares algorithm (Q1746492) (← links)
- A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation (Q1746556) (← links)
- Sparse recovery in probability via \(l_q\)-minimization with Weibull random matrices for \(0 < q\leq 1\) (Q1747365) (← links)
- A probabilistic learning algorithm for robust modeling using neural networks with random weights (Q1749195) (← links)
- DC formulations and algorithms for sparse optimization problems (Q1749449) (← links)
- Minimization of transformed \(L_1\) penalty: theory, difference of convex function algorithm, and robust application in compressed sensing (Q1749455) (← links)
- Iterative reweighted methods for \(\ell _1-\ell _p\) minimization (Q1753073) (← links)
- The noncooperative transportation problem and linear generalized Nash games (Q1754116) (← links)
- Support vector machines regression with \(l^1\)-regularizer (Q1759352) (← links)
- On the sparseness of 1-norm support vector machines (Q1784565) (← links)
- On finding a generalized lowest rank solution to a linear semi-definite feasibility problem (Q1785372) (← links)
- Approximately normalized iterative hard thresholding for nonlinear compressive sensing (Q1792868) (← links)
- Compressive sensing in signal processing: algorithms and transform domain formulations (Q1793545) (← links)
- Efficient extreme learning machine via very sparse random projection (Q1797950) (← links)
- An iterative algorithm for fitting nonconvex penalized generalized linear models with grouped predictors (Q1927082) (← links)
- The convex geometry of linear inverse problems (Q1928276) (← links)
- Linear program relaxation of sparse nonnegative recovery in compressive sensing microarrays (Q1929588) (← links)
- Strengthening hash families and compressive sensing (Q1932362) (← links)
- Robust estimation for an inverse problem arising in multiview geometry (Q1932929) (← links)
- Full spark frames (Q1934656) (← links)
- Sparse total least squares: analysis and greedy algorithms (Q1938584) (← links)
- Compressed sensing and matrix completion with constant proportion of corruptions (Q1939501) (← links)
- An efficient algorithm for \(\ell_{0}\) minimization in wavelet frame based image restoration (Q1945380) (← links)
- Minimax risks for sparse regressions: ultra-high dimensional phenomenons (Q1950804) (← links)
- Estimation of Gaussian graphs by model selection (Q1951762) (← links)
- On the conditions used to prove oracle results for the Lasso (Q1952029) (← links)
- The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso) (Q1952206) (← links)
- Restricted \(p\)-isometry properties of partially sparse signal recovery (Q1956098) (← links)
- A short note on compressed sensing with partially known signal support (Q1957941) (← links)
- Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling (Q1979911) (← links)
- Kernel conjugate gradient methods with random projections (Q1979923) (← links)
- A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness (Q1987969) (← links)
- On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems (Q1988352) (← links)
- The landscape of empirical risk for nonconvex losses (Q1991675) (← links)
- Uniqueness of the minimal \(l_1\)-norm solution to the monotone linear complementarity problem (Q1993073) (← links)
- Sparse approximation of fitting surface by elastic net (Q1993576) (← links)
- Optimality conditions for locally Lipschitz optimization with \(l_0\)-regularization (Q1996752) (← links)
- Sparse polynomial interpolation: sparse recovery, super-resolution, or Prony? (Q2000529) (← links)
- Sparse approximate reconstruction decomposed by two optimization problems (Q2003320) (← links)
- An algebraic perspective on integer sparse recovery (Q2007643) (← links)
- Computing the spark: mixed-integer programming for the (vector) matroid girth problem (Q2007824) (← links)
