Pages that link to "Item:Q4834376"

The following pages link to Sparse Approximate Solutions to Linear Systems (Q4834376):

Displaying 50 items.

• 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)
• Dictionary evaluation and optimization for sparse coding based speech processing (Q1749610) (← links)
• Matrix completion under interval uncertainty (Q1752160) (← links)
• Recovery of seismic wavefields by an \(l_{q}\)-norm constrained regularization method (Q1785033) (← links)
• Capped \(\ell_p\) approximations for the composite \(\ell_0\) regularization problem (Q1785036) (← links)
• Alternating direction method of multipliers for truss topology optimization with limited number of nodes: a cardinality-constrained second-order cone programming approach (Q1787322) (← links)
• A novel detection scheme with multiple observations for sparse signal based on likelihood ratio test with sparse estimation (Q1793681) (← links)
• Structured overcomplete sparsifying transform learning with convergence guarantees and applications (Q1799975) (← links)
• Computing sparse approximations deterministically (Q1915603) (← links)
• Linear program relaxation of sparse nonnegative recovery in compressive sensing microarrays (Q1929588) (← links)
• Strengthening hash families and compressive sensing (Q1932362) (← links)
• Sparsity driven people localization with a heterogeneous network of cameras (Q1932873) (← links)
• Accelerated linearized Bregman method (Q1945379) (← links)
• Sparse signal recovery via ECME thresholding pursuits (Q1954822) (← links)
• Analysis of the ratio of \(\ell_1\) and \(\ell_2\) norms in compressed sensing (Q1979938) (← links)
• A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness (Q1987969) (← links)
• Restricted strong convexity implies weak submodularity (Q1990594) (← links)
• Optimality conditions for locally Lipschitz optimization with \(l_0\)-regularization (Q1996752) (← links)
• Sparse approximate reconstruction decomposed by two optimization problems (Q2003320) (← links)
• Analysis of the self projected matching pursuit algorithm (Q2005321) (← links)
• Salt and pepper noise removal based on an approximation of \(l_0\) norm (Q2006223) (← links)
• Computing the spark: mixed-integer programming for the (vector) matroid girth problem (Q2007824) (← links)
• Deterministic constructions of compressed sensing matrices based on optimal codebooks and codes (Q2008183) (← links)
• Surrogate optimization for \(p\)-norms (Q2010926) (← links)
• An efficient algorithm based on sparse optimization for the aircraft departure scheduling problem (Q2013610) (← links)
• Complexity and applications of the homotopy principle for uniformly constrained sparse minimization (Q2019907) (← links)
• Optimization problems for machine learning: a survey (Q2029894) (← links)
• Convergent inexact penalty decomposition methods for cardinality-constrained problems (Q2031962) (← links)
• The horseshoe-like regularization for feature subset selection (Q2040669) (← links)
• Subspace quadratic regularization method for group sparse multinomial logistic regression (Q2044487) (← links)
• Solving nonnegative sparsity-constrained optimization via DC quadratic-piecewise-linear approximations (Q2052409) (← links)
• A sparse optimization problem with hybrid \(L_2\)-\(L_p\) regularization for application of magnetic resonance brain images (Q2060052) (← links)
• Adaboost-based ensemble of polynomial chaos expansion with adaptive sampling (Q2060149) (← links)
• Sparse approximate solutions to max-plus equations (Q2061850) (← links)
• On the computational complexity of the secure state-reconstruction problem (Q2063856) (← links)
• Provably optimal sparse solutions to overdetermined linear systems with non-negativity constraints in a least-squares sense by implicit enumeration (Q2069147) (← links)
• Sparse classification: a scalable discrete optimization perspective (Q2071494) (← links)
• IDPCNN: iterative denoising and projecting CNN for MRI reconstruction (Q2074885) (← links)
• Robust subset selection (Q2076115) (← links)
• New bounds for subset selection from conic relaxations (Q2076815) (← links)
• Flexible construction of measurement matrices in compressed sensing based on extensions of incidence matrices of combinatorial designs (Q2078717) (← links)
• Nonparametric regression with modified ReLU networks (Q2081757) (← links)
• Variable selection in convex quantile regression: \(\mathcal{L}_1\)-norm or \(\mathcal{L}_0\)-norm regularization? (Q2083962) (← links)
• Commonsense explanations of sparsity, Zipf law, and Nash's bargaining solution (Q2086143) (← links)
• A Lagrange-Newton algorithm for sparse nonlinear programming (Q2089792) (← links)
• Sparse regression at scale: branch-and-bound rooted in first-order optimization (Q2097642) (← links)
• Convex optimization under combinatorial sparsity constraints (Q2102824) (← links)
• A linear system output transformation for sparse approximation (Q2103842) (← links)