Results 11 to 20 of about 1,485 (240)
A Stochastic Proximal Alternating Minimization for Nonsmooth and Nonconvex Optimization [PDF]
SIAM Journal on Imaging Sciences, 2021 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Driggs, Derek +4 more
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The Proximal Alternating Minimization Algorithm for Two-Block Separable Convex Optimization Problems with Linear Constraints. [PDF]
J Optim Theory Appl, 2019 The Alternating Minimization Algorithm (AMA) has been proposed by Tseng to solve convex programming problems with two-block separable linear constraints and objectives, whereby (at least) one of the components of the latter is assumed to be strongly convex.
Bitterlich S +3 more
europepmc +6 more sources
Semi-Linearized Proximal Alternating Minimization for a Discrete Mumford–Shah Model [PDF]
IEEE Transactions on Image Processing, 2020 The Mumford-Shah model is a standard model in image segmentation, and due to its difficulty, many approximations have been proposed. The major interest of this functional is to enable joint image restoration and contour detection. In this work, we propose a general formulation of the discrete counterpart of the Mumford-Shah functional, adapted to ...
Marion Foare +2 more
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A Proximal Alternating Direction Method of Multiplier for Linearly Constrained Nonconvex Minimization [PDF]
SIAM Journal on Optimization, 2020 Consider the minimization of a nonconvex differentiable function over a polyhedron. A popular primal-dual first-order method for this problem is to perform a gradient projection iteration for the augmented Lagrangian function and then update the dual multiplier vector using the constraint residual.
Jiawei Zhang 0007, Zhi-Quan Luo
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The convergence properties of infeasible inexact proximal alternating linearized minimization
Science China Mathematics, 2023 The proximal alternating linearized minimization method (PALM) suits well for solving block-structured optimization problems, which are ubiquitous in real applications. In the cases where subproblems do not have closed-form solutions, e.g., due to complex constraints, infeasible subsolvers are indispensable, giving rise to an infeasible inexact PALM ...
Hu, Yukuan, Liu, Xin
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The PRIMPING routine—Tiling through proximal alternating linearized minimization [PDF]
Data Mining and Knowledge Discovery, 2017 Mining and exploring databases should provide users with knowledge and new insights. Tiles of data strive to unveil true underlying structure and distinguish valuable information from various kinds of noise. We propose a novel Boolean matrix factorization algorithm to solve the tiling problem, based on recent results from optimization theory.
Sibylle Hess +2 more
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Inertial Proximal Deep Learning Alternating Minimization for Efficient Neutral Network Training [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 In recent years, the Deep Learning Alternating Minimization (DLAM), which is actually the alternating minimization applied to the penalty form of the deep neutral networks training, has been developed as an alternative algorithm to overcome several drawbacks of Stochastic Gradient Descent (SGD) algorithms.
Linbo Qiao +3 more
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IET Image Processing, 2023 Image restoration with impulse noise is an important task in image processing. Taking into account the statistical distribution of impulse noise, the ℓ1‐norm data fidelity and total variation (ℓ1TV) model has been widely used in this area.
Yuchao Tang +3 more
doaj +1 more source
CoRR, 2023 Building upon recent works on linesearch-free adaptive proximal gradient methods, this paper proposes adaPG$^{q,r}$, a framework that unifies and extends existing results by providing larger stepsize policies and improved lower bounds. Different choices of the parameters $q$ and $r$ are discussed and the efficacy of the resulting methods is ...
Latafat, P. +2 more
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Nonlocal Block-Term Decomposition for Hyperspectral Image Mixed Noise Removal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Since the facility restrictions and weather conditions, hyperspectral image (HSI) is generally seriously polluted by a variety of noises. Recently, the method based on block-term decomposition with rank-$(L, L, 1)$ (BTD) has attracted wide attention in ...
Zeyu Zeng +3 more
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