A survey on some recent developments of alternating direction method of multipliers
DR Han - Journal of the Operations Research Society of China, 2022 - Springer
Recently, alternating direction method of multipliers (ADMM) attracts much attentions from
various fields and there are many variant versions tailored for different models. Moreover, its …
various fields and there are many variant versions tailored for different models. Moreover, its …
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 …
Matrix factorization techniques in machine learning, signal processing, and statistics
Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …
compressible signals. Sparse coding represents a signal as a sparse linear combination of …
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
PL Loh, MJ Wainwright - Advances in Neural Information …, 2013 - proceedings.neurips.cc
We establish theoretical results concerning all local optima of various regularized M-
estimators, where both loss and penalty functions are allowed to be nonconvex. Our results …
estimators, where both loss and penalty functions are allowed to be nonconvex. Our results …
[图书][B] Statistical foundations of data science
Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …
statistical models, contemporary statistical machine learning techniques and algorithms …
Linearized ADMM for nonconvex nonsmooth optimization with convergence analysis
Linearized alternating direction method of multipliers (ADMM) as an extension of ADMM has
been widely used to solve linearly constrained problems in signal processing, machine …
been widely used to solve linearly constrained problems in signal processing, machine …
Total variation denoising via the Moreau envelope
I Selesnick - IEEE Signal Processing Letters, 2017 - ieeexplore.ieee.org
Total variation denoising is a nonlinear filtering method well suited for the estimation of
piecewise-constant signals observed in additive white Gaussian noise. The method is …
piecewise-constant signals observed in additive white Gaussian noise. The method is …
Logarithmic norm regularized low-rank factorization for matrix and tensor completion
L Chen, X Jiang, X Liu, Z Zhou - IEEE Transactions on Image …, 2021 - ieeexplore.ieee.org
Matrix and tensor completion aim to recover the incomplete two-and higher-dimensional
observations using the low-rank property. Conventional techniques usually minimize the …
observations using the low-rank property. Conventional techniques usually minimize the …
Robust low-rank tensor recovery via nonconvex singular value minimization
L Chen, X Jiang, X Liu, Z Zhou - IEEE Transactions on Image …, 2020 - ieeexplore.ieee.org
Tensor robust principal component analysis via tensor nuclear norm (TNN) minimization has
been recently proposed to recover the low-rank tensor corrupted with sparse noise/outliers …
been recently proposed to recover the low-rank tensor corrupted with sparse noise/outliers …
A survey of admm variants for distributed optimization: Problems, algorithms and features
By coordinating terminal smart devices or microprocessors to engage in cooperative
computation to achieve systemlevel targets, distributed optimization is incrementally favored …
computation to achieve systemlevel targets, distributed optimization is incrementally favored …