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Dual descent regularization algorithms in variable exponent Lebesgue spaces for imaging
We consider one-step iterative algorithms to solve ill-posed inverse problems in the framework of variable exponent Lebesgue spaces
L p (⋅) . These...
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Communication-Efficient Distributed Minimax Optimization via Markov Compression
Recently, the minimax problem has attracted a lot of attention due to its wide applications in modern machine learning fields such as GANs. With the...
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On Active-Set LP Algorithms Allowing Basis Deficiency
An interesting phenomenon in linear programming (LP) is how to deal with solutions in which the number of nonzero variables is less than the number...
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A dual symmetric Gauss-Seidel alternating direction method of multipliers for hyperspectral sparse unmixing
Since sparse unmixing has emerged as a promising approach to hyperspectral unmixing, some spatial-contextual information in the hyperspectral images...
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Landmark-Guided Conditional GANs for Face Aging
Face aging, which alters a person’s facial photo to the appearance at a different age, is a popular topic in multimedia applications. Recently,...
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A Variational Model for Deformable Registration of Uni-modal Medical Images with Intensity Biases
Deformable image registration aims at estimating a proper displacement field from a fixed image and a moving one. Variational deformable registration...
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A prediction–correction-based primal–dual hybrid gradient method for linearly constrained convex minimization
The primal–dual hybrid gradient (PDHG) method has been widely used for solving saddle point problems emerged in imaging processing. In particular,...
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Adaptive Parallel Average Schwarz Preconditioner for Crouzeix-Raviart Finite Volume Method
In this paper, we describe and analyze an Average Schwarz Method with spectrally enriched coarse space for a Crouzeix-Raviart finite volume element...
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Predictive Online Optimisation with Applications to Optical Flow
Online optimisation revolves around new data being introduced into a problem while it is still being solved; think of deep learning as more training...
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Extension of the LP-Newton method to conic programming problems via semi-infinite representation
The LP-Newton method solves linear programming (LP) problems by repeatedly projecting a current point onto a certain relevant polytope. In this...
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Optimization of Fuzzy C-Means with Alternating Direction Method of Multipliers
Among the clustering methods, K-Means and its variants are very popular. These methods solve at each iteration the first-order optimality conditions....
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A dual RAMP algorithm for single source capacitated facility location problems
In this paper, we address the Single Source Capacitated Facility Location Problem (SSCFLP) which considers a set of possible locations for opening...
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A lagrangian-based approach for universum twin bounded support vector machine with its applications
The Universum provides prior knowledge about data in the mathematical problem to improve the generalization performance of the classifiers. Several...
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Total generalized variational-liked network for image denoising
Deep convolutional neural networks (DCNN) have been widely used in the field of image denoising because of their fast inference and good performance....
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A Combinatorial Cut-Toggling Algorithm for Solving Laplacian Linear Systems
Over the last two decades, a significant line of work in theoretical algorithms has made progress in solving linear systems of the form
... 
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Least squares approach to K-SVCR multi-class classification with its applications
The support vector classification-regression machine for K-class classification (K-SVCR) is a novel multi-class classification method based on the...
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A novel image denoising approach based on a non-convex constrained PDE: application to ultrasound images
In this paper, we are interested in the mathematical and simulation study of a new non-convex constrained PDE to remove the mixture of...
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Model Learning: Primal Dual Networks for Fast MR Imaging
Magnetic resonance imaging (MRI) is known to be a slow imaging modality and undersampling in k-space has been used to increase the imaging speed....
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Parallel Alternating Derection Method of Multipliers with Application to Image Restoration
Compound regularization methods can combine the advantages of multiple regularization means to obtain superior results, but this often leads to more...
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Adaptive Localized Reduced Basis Methods for Large Scale PDE-Constrained Optimization
In this contribution, we introduce and numerically evaluate a certified and adaptive localized reduced basis method as a local model in a...