Pages that link to "Item:Q2706429"

The following pages link to Atomic decomposition by basis pursuit (Q2706429):

Displaying 50 items.

• ADMM-softmax: an ADMM approach for multinomial logistic regression (Q1988494) (← links)
• Approximate \(\ell_0\)-penalized estimation of piecewise-constant signals on graphs (Q1990576) (← links)
• Compressing sensing based source localization for controlled acoustic signals using distributed microphone arrays (Q1992459) (← links)
• An automatic and parameter-free information-based method for sparse representation in wavelet bases (Q1998046) (← links)
• Measurement matrix optimization via mutual coherence minimization for compressively sensed signals reconstruction (Q2004244) (← links)
• Solution paths for the generalized Lasso with applications to spatially varying coefficients regression (Q2008112) (← links)
• A general self-adaptive relaxed-PPA method for convex programming with linear constraints (Q2015595) (← links)
• Novel sparseness-inducing dual Kalman filter and its application to tracking time-varying spatially-sparse structural stiffness changes and inputs (Q2021040) (← links)
• Sparse identification of nonlinear dynamical systems via reweighted \(\ell_1\)-regularized least squares (Q2021994) (← links)
• A golden ratio primal-dual algorithm for structured convex optimization (Q2025858) (← links)
• Convergence study on strictly contractive peaceman-Rachford splitting method for nonseparable convex minimization models with quadratic coupling terms (Q2026767) (← links)
• An efficient descent method for locally Lipschitz multiobjective optimization problems (Q2031935) (← links)
• Low-rank matrix recovery via regularized nuclear norm minimization (Q2036488) (← links)
• Robust detection of neural spikes using sparse coding based features (Q2038824) (← links)
• A Laplacian approach to \(\ell_1\)-norm minimization (Q2044482) (← links)
• Accelerating the Bayesian inference of inverse problems by using data-driven compressive sensing method based on proper orthogonal decomposition (Q2055173) (← links)
• Optimal representative sample weighting (Q2058720) (← links)
• An improved linear convergence of FISTA for the LASSO problem with application to CT image reconstruction (Q2060059) (← links)
• Detecting and identifying anomalous effects in complex signals (Q2069676) (← links)
• Smoothing Newton method for \(\ell^0\)-\(\ell^2\) regularized linear inverse problem (Q2072164) (← links)
• Sparsest piecewise-linear regression of one-dimensional data (Q2074905) (← links)
• Structured iterative hard thresholding with on- and off-grid applications (Q2074952) (← links)
• Frame soft shrinkage operators are proximity operators (Q2075005) (← links)
• The all-or-nothing phenomenon in sparse linear regression (Q2078961) (← links)
• Partial gradient optimal thresholding algorithms for a class of sparse optimization problems (Q2079693) (← links)
• An inexact symmetric ADMM algorithm with indefinite proximal term for sparse signal recovery and image restoration problems (Q2088791) (← links)
• A weighted randomized sparse Kaczmarz method for solving linear systems (Q2099537) (← links)
• LASSO for streaming data with adaptative filtering (Q2104007) (← links)
• Understanding neural networks with reproducing kernel Banach spaces (Q2105111) (← links)
• Design of c-optimal experiments for high-dimensional linear models (Q2108501) (← links)
• Stable high-order cubature formulas for experimental data (Q2133503) (← links)
• Sparse Bayesian learning for network structure reconstruction based on evolutionary game data (Q2137626) (← links)
• On LASSO for predictive regression (Q2155298) (← links)
• Efficient uncertainty quantification of stochastic problems in CFD by combination of compressed sensing and POD-kriging (Q2156798) (← links)
• A data-driven line search rule for support recovery in high-dimensional data analysis (Q2157522) (← links)
• Inertial accelerated primal-dual methods for linear equality constrained convex optimization problems (Q2159429) (← links)
• Autoencoders reloaded (Q2165369) (← links)
• Sparse minimax portfolio and Sharpe ratio models (Q2165774) (← links)
• The springback penalty for robust signal recovery (Q2168687) (← links)
• Generalizing CoSaMP to signals from a union of low dimensional linear subspaces (Q2175016) (← links)
• Towards understanding sparse filtering: a theoretical perspective (Q2179299) (← links)
• Online sequential echo state network with sparse RLS algorithm for time series prediction (Q2185621) (← links)
• Sparse signal reconstruction via the approximations of \(\ell_0\) quasinorm (Q2190319) (← links)
• Iterative hard thresholding for compressed data separation (Q2190470) (← links)
• Analysis non-sparse recovery for relaxed ALASSO (Q2191835) (← links)
• Adaptive decomposition-based evolutionary approach for multiobjective sparse reconstruction (Q2198239) (← links)
• Effective zero-norm minimization algorithms for noisy compressed sensing (Q2198627) (← links)
• Atomic norm minimization for decomposition into complex exponentials and optimal transport in Fourier domain (Q2202787) (← links)
• Primal-dual optimization algorithms over Riemannian manifolds: an iteration complexity analysis (Q2205985) (← links)
• Stability of 1-bit compressed sensing in sparse data reconstruction (Q2217038) (← links)