Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems

By Mário A. T. Figueiredo, Robert D. Nowak, Stephen J. Wright. IEEE Journal on Selected Topics in Signal Processing, 2007

3,632 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/34fe87515826dce400f0b3090c4cdd638bc3277b

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Citations in the corpus

Fault Identification Via Nonparametric Belief Propagation (2009, methodology): “Third, we ran several non-Bayesian CS algorithms: (i) CoSaMP [ 21]; (ii ) GPSR [22]; and (iii ) iterative hard thresholding (HardIO) [ 23].”
A self adaptive inertial subgradient extragradient algorithm for variational inequality and common fixed point of multivalued mappings in Hilbert spaces (2019, background): “for any nonnegative real number t (see [49]).”
Fixed point and Bregman iterative methods for matrix rank minimization (2009, methodology): “6) have been widely investigat ed nd many algorithms have been suggested including convex optimization methods ([2; 10; 20; 24; 27]) and heuris tic methods ([13; 15; 16; 39; 40]).”
Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation (2014, neutral): “Many algorithms [26], [42], [43], [44], have”
Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors (2011, methodology): “Instead, we solve Basis Pursuit (BP) by relaxing the objective in (RCS) to the `1-norm; the result is a convex, polynomial-time algorithm [6].”
Sparse coding based visual tracking: Review and experimental comparison (2013, background): “(4) as a quadratic programming problem, namely, gradient projection sparse representation (GPSR) [24] and truncated Newton interior-point method (TNIPM) [25].”
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals (2009, background): “Recently developed algorithms [17], [21], [31]–[33] typically produce good solutions with a few hundred applications of the system matrix.”
High-resolution prestack seismic inversion using a hybrid FISTA least-squares strategy (2013, methodology): “LS stage Once the support of the solution has been obtained using FISTA, we perform a debiasing step (Figueiredo et al., 2007) to adjust the High-resolution prestack inversion R187”
Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit (2007, background): “Much promising work has been done on the complexity of linear programming techniques [1], and their applications to compressed sensing (see [15], [2], [2 4]).”
A Modified Hestenes-Stiefel-Type Derivative-Free Method for Large-Scale Nonlinear Monotone Equations (2020, background): “Monotone systems of equations can also be applied in L1-norm regularization sparse optimization problems (see [3,4]) and discrete mathematics such as graph theory (see [5,6]).”
Image denoising with nonlocal spectral graph wavelets (2010, neutral): “This is a BPDN/Lasso problem in Lagrangian form with a synthesis sparsity prior and thus admits a global minimum which can be found by well established minimization techniques like the iterative soft thresholding (IST) [41], the forward-backward (FB) splitting [42], or related approaches [43, 44].”