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Proximal regularization for the saddle point gradient dynamics

Goldsztajn, Diego; Paganini, Fernando (2021)

This paper concerns the solution of a convex optimization problem through the saddle point gradient dynamics. Instead of using the standard Lagrangian as is classical in this method, we consider a regularized Lagrangian ...

Convergence to the optimal value for barrier methods combined with hessian riemannian gradient flows and generalized proximal algorithms

Autor desconocido (HELDERMANN VERLAG, 2010)

Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging

New York Univ; Universidade Estadual Paulista (Unesp); CUNY (Ieee-inst Electrical Electronics Engineers Inc, 2019-03-01)

An improvement of the monotone fast iterative shrinkage-thresholding algorithm (MFISTA) for faster convergence is proposed in this paper. Our motivation is to reduce the reconstruction time of compressed sensing problems ...

Proximal methods for nonlinear programming: double regularization and inexact subproblems

ECKSTEIN, Jonathan; SILVA, Paulo J. S. (SPRINGER, 2010)

This paper describes the first phase of a project attempting to construct an efficient general-purpose nonlinear optimizer using an augmented Lagrangian outer loop with a relative error criterion, and an inner loop employing ...

Hessian Riemannian gradient flows in convex programming

Álvarez Daziano, Felipe; Bolte, Jérome; Brahic, Olivier (SIAM PUBLICATIONS, 2004)

In view of solving theoretically constrained minimization problems, we investigate the properties of the gradient flows with respect to Hessian Riemannian metrics induced by Legendre functions. The first result characterizes ...

Convergence to the optimal value for barrier methods combined with hessian riemannian gradient flows and generalized proximal algorithmsJOURNAL OF CONVEX ANALYSISJ CONVEX ANAL

Alvarez, Felipe; Lopez, Julio (HELDERMANN VERLAG, 2016)

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Proximal regularization for the saddle point gradient dynamics

Convergence to the optimal value for barrier methods combined with hessian riemannian gradient flows and generalized proximal algorithms

Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging

Proximal methods for nonlinear programming: double regularization and inexact subproblems

Hessian Riemannian gradient flows in convex programming

Convergence to the optimal value for barrier methods combined with hessian riemannian gradient flows and generalized proximal algorithmsJOURNAL OF CONVEX ANALYSISJ CONVEX ANAL

Spectral projected gradient and variable metric methods for optimization with linear inequalities

Um algoritmo de ponto proximal com efeitos de inércia e relaxação

Efficient algorithms for convolutional dictionary learning via accelerated proximal gradient

Efficient algorithms for convolutional dictionary learning via accelerated proximal gradient

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Proximal regularization for the saddle point gradient dynamics

Convergence to the optimal value for barrier methods combined with hessian riemannian gradient flows and generalized proximal algorithms

Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging

Proximal methods for nonlinear programming: double regularization and inexact subproblems

Hessian Riemannian gradient flows in convex programming

Convergence to the optimal value for barrier methods combined with hessian riemannian gradient flows and generalized proximal algorithmsJOURNAL OF CONVEX ANALYSISJ CONVEX ANAL

Spectral projected gradient and variable metric methods for optimization with linear inequalities

Um algoritmo de ponto proximal com efeitos de inércia e relaxação

Efficient algorithms for convolutional dictionary learning via accelerated proximal gradient

Efficient algorithms for convolutional dictionary learning via accelerated proximal gradient