Sparse regularization via convex analysis
I Selesnick - IEEE Transactions on Signal Processing, 2017 - ieeexplore.ieee.org
Sparse approximate solutions to linear equations are classically obtained via L1 norm
regularized least squares, but this method often underestimates the true solution. As an …
regularized least squares, but this method often underestimates the true solution. As an …
The sliding Frank–Wolfe algorithm and its application to super-resolution microscopy
This paper showcases the theoretical and numerical performance of the Sliding Frank–
Wolfe, which is a novel optimization algorithm to solve the BLASSO sparse spikes super …
Wolfe, which is a novel optimization algorithm to solve the BLASSO sparse spikes super …
A Unified View of Exact Continuous Penalties for - Minimization
Numerous nonconvex continuous penalties have been proposed to approach the \ell_0
pseudonorm for optimization purpose. Apart from the theoretical results for convex \ell_1 …
pseudonorm for optimization purpose. Apart from the theoretical results for convex \ell_1 …
Sparse signal approximation via nonseparable regularization
I Selesnick, M Farshchian - IEEE Transactions on Signal …, 2017 - ieeexplore.ieee.org
The calculation of a sparse approximate solution to a linear system of equations is often
performed using either L1-norm regularization and convex optimization or nonconvex …
performed using either L1-norm regularization and convex optimization or nonconvex …
Sparse and imperceptible adversarial attack via a homotopy algorithm
Sparse adversarial attacks can fool deep neural networks (DNNs) by only perturbing a few
pixels (regularized by $\ell_0 $ norm). Recent efforts combine it with another $\ell_\infty …
pixels (regularized by $\ell_0 $ norm). Recent efforts combine it with another $\ell_\infty …
New Insights on the Optimality Conditions of the Minimization Problem
This paper is devoted to the analysis of necessary (not sufficient) optimality conditions for the
ℓ _0 ℓ 0-regularized least-squares minimization problem. Such conditions are the roots of …
ℓ _0 ℓ 0-regularized least-squares minimization problem. Such conditions are the roots of …
Enhanced sparsity by non-separable regularization
IW Selesnick, I Bayram - IEEE Transactions on Signal …, 2016 - ieeexplore.ieee.org
This paper develops a convex approach for sparse one-dimensional deconvolution that
improves upon L1-norm regularization, the standard convex approach. We propose a …
improves upon L1-norm regularization, the standard convex approach. We propose a …
Energy efficient data collection in large-scale internet of things via computation offloading
Internet of Things (IoT) can be used to promote many advanced applications by utilizing the
sensed data collected from various settings. To reduce the energy consumption of IoT …
sensed data collected from various settings. To reduce the energy consumption of IoT …
Sparse ECG denoising with generalized minimax concave penalty
Z Jin, A Dong, M Shu, Y Wang - Sensors, 2019 - mdpi.com
The electrocardiogram (ECG) is an important diagnostic tool for cardiovascular diseases.
However, ECG signals are susceptible to noise, which may degenerate waveform and …
However, ECG signals are susceptible to noise, which may degenerate waveform and …
Global optimization for sparse solution of least squares problems
R Ben Mhenni, S Bourguignon… - Optimization Methods and …, 2022 - Taylor & Francis
Finding solutions to least-squares problems with low cardinality has found many
applications, including portfolio optimization, subset selection in statistics, and inverse …
applications, including portfolio optimization, subset selection in statistics, and inverse …