[PDF][PDF] Computing the proximal operator of the ℓ1, q-norm for group sparsity
In this note, we comprehensively characterize the proximal operator of the ℓ1, q-norm with
0< q< 1 by exploiting the well-known proximal operator of the ℓq-norm on the real line. In …
0< q< 1 by exploiting the well-known proximal operator of the ℓq-norm on the real line. In …
Sparse Recovery: The Square of Norms
This paper introduces a nonconvex approach for sparse signal recovery, proposing a novel
model termed the\(\tau _2\)-model, which utilizes the squared\(\ell _1/\ell _2\) norms for this …
model termed the\(\tau _2\)-model, which utilizes the squared\(\ell _1/\ell _2\) norms for this …
On Choosing Initial Values of Iteratively Reweighted Algorithms for the Piece-wise Exponential Penalty
R Lin, S Li, Y Liu - arXiv preprint arXiv:2310.17849, 2023 - arxiv.org
Computing the proximal operator of the sparsity-promoting piece-wise exponential (PiE)
penalty $1-e^{-| x|/\sigma} $ with a given shape parameter $\sigma> 0$, which is treated as …
penalty $1-e^{-| x|/\sigma} $ with a given shape parameter $\sigma> 0$, which is treated as …
Computing the Proximal Operator of the -norm for Group Sparsity
In this note, we comprehensively characterize the proximal operator of the $\ell_ {1, q} $-
norm with $0\!<\! q\!<\! 1$ by exploiting the well-known proximal operator of the $\ell_q …
norm with $0\!<\! q\!<\! 1$ by exploiting the well-known proximal operator of the $\ell_q …
Power of -Norm Regularized Kaczmarz Algorithms for High-Order Tensor Recovery
K Henneberger, J Qin - arXiv preprint arXiv:2405.08275, 2024 - arxiv.org
Tensors serve as a crucial tool in the representation and analysis of complex, multi-
dimensional data. As data volumes continue to expand, there is an increasing demand for …
dimensional data. As data volumes continue to expand, there is an increasing demand for …
Hyperspectral Band Selection based on Generalized 3DTV and Tensor CUR Decomposition
K Henneberger, J Qin - arXiv preprint arXiv:2405.00951, 2024 - arxiv.org
Hyperspectral Imaging (HSI) serves as an important technique in remote sensing. However,
high dimensionality and data volume typically pose significant computational challenges …
high dimensionality and data volume typically pose significant computational challenges …
Sparse Recovery: The Square of Norms
This paper introduces a nonconvex approach for sparse signal recovery, proposing a novel
model termed the τ 2-model, which utilizes the squared ℓ 1/ℓ 2 norms for this purpose. Our …
model termed the τ 2-model, which utilizes the squared ℓ 1/ℓ 2 norms for this purpose. Our …