A Method for Finding Structured Sparse Solutions to Nonnegative Least Squares Problems with Applications

DOI10.1137/13090540XzbMath1282.90239arXiv1301.0413OpenAlexW2004886822MaRDI QIDQ2873273

Publication date: 23 January 2014

Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1301.0413

zbMATH Keywords

nonnegative least squares hyperspectral imaging structured sparsity basis pursuit difference of convex programming unmixing differential optical absorption spectroscopy scaled gradient projection

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Numerical optimization and variational techniques (65K10) Methods of successive quadratic programming type (90C55) Inverse problems in optimal control (49N45)

Related Items (48)

Unconstrained $\ell_1$-$\ell_2$ minimization for sparse recovery via mutual coherence ⋮ Three $l_1$ based nonconvex methods in constructing sparse mean reverting portfolios ⋮ A unified DC programming framework and efficient DCA based approaches for large scale batch reinforcement learning ⋮ DC programming and DCA for solving Brugnano-Casulli piecewise linear systems ⋮ $l_1$-$l_2$ regularization of split feasibility problems ⋮ Minimization of $L_1$ Over $L_2$ for Sparse Signal Recovery with Convergence Guarantee ⋮ Iterative positive thresholding algorithm for non-negative sparse optimization ⋮ A Weighted Difference of Anisotropic and Isotropic Total Variation Model for Image Processing ⋮ The springback penalty for robust signal recovery ⋮ An Iterative Reduction FISTA Algorithm for Large-Scale LASSO ⋮ An optimal subgradient algorithm for large-scale bound-constrained convex optimization ⋮ A necessary and sufficient condition for sparse vector recovery via $\ell_1-\ell_2$ minimization ⋮ Smoothing inertial projection neural network for minimization $L_{p-q}$ in sparse signal reconstruction ⋮ Multicompartment magnetic resonance fingerprinting ⋮ Efficient color image segmentation via quaternion-based $L_1/L_2$ Regularization ⋮ Transformed $\ell_1$ regularization for learning sparse deep neural networks ⋮ A smoothing inertial neural network for sparse signal reconstruction with noise measurements via $L_p-L_1$ minimization ⋮ Block-sparse recovery and rank minimization using a weighted $l_p-l_q$ model ⋮ Sparse signal reconstruction via the approximations of $\ell_0$ quasinorm ⋮ Study on $L_1$ over $L_2$ Minimization for nonnegative signal recovery ⋮ Structured model selection via ℓ1−ℓ2 optimization ⋮ Nonconvex $\ell_p-\alpha\ell_q$ minimization method and $p$-RIP condition for stable recovery of approximately $k$-sparse signals ⋮ A reduced half thresholding algorithm ⋮ Open issues and recent advances in DC programming and DCA ⋮ An iDCA with sieving strategy for PDE-constrained optimization problems with $L^{1-2}$-control cost ⋮ Morozov's discrepancy principle for $\alpha\ell_1-\beta\ell_2$ sparsity regularization ⋮ A class of null space conditions for sparse recovery via nonconvex, non-separable minimizations ⋮ Heuristic discrepancy principle for variational regularization of inverse problems ⋮ Computing sparse representation in a highly coherent dictionary based on difference of $L_1$ and $L_2$ ⋮ Analysis of the ratio of $\ell_1$ and $\ell_2$ norms in compressed sensing ⋮ Fast L1-L2 minimization via a proximal operator ⋮ DC programming and DCA: thirty years of developments ⋮ Minimization of transformed $L_1$ penalty: theory, difference of convex function algorithm, and robust application in compressed sensing ⋮ Solving structured nonsmooth convex optimization with complexity $\mathcal {O}(\varepsilon ^{-1/2})$ ⋮ Sparse solution of nonnegative least squares problems with applications in the construction of probabilistic Boolean networks ⋮ Total Variation Regularization Strategies in Full-Waveform Inversion ⋮ A Scale-Invariant Approach for Sparse Signal Recovery ⋮ High-dimensional sign-constrained feature selection and grouping ⋮ $ \newcommand{\e}{{\rm e}} {\alpha\ell_{1}-\beta\ell_{2}}$ regularization for sparse recovery ⋮ An Efficient Proximal Block Coordinate Homotopy Method for Large-Scale Sparse Least Squares Problems ⋮ Reconstruction of jointly sparse vectors via manifold optimization ⋮ Penalized robust estimators in sparse logistic regression ⋮ ℓ ₁ − αℓ ₂ minimization methods for signal and image reconstruction with impulsive noise removal ⋮ A Three-Operator Splitting Algorithm for Nonconvex Sparsity Regularization ⋮ Minimization of $\ell_{1-2}$ for Compressed Sensing ⋮ Truncated $l_{1-2}$ Models for Sparse Recovery and Rank Minimization ⋮ The proximity operator of the log-sum penalty ⋮ A new sufficient condition for sparse vector recovery via ℓ1 − ℓ2 local minimization

This page was built for publication: A Method for Finding Structured Sparse Solutions to Nonnegative Least Squares Problems with Applications