Splitting proximal with penalization schemes for additive convex hierarchical minimization problems
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Publication:5858997
DOI10.1080/10556788.2018.1556660OpenAlexW2903655765WikidataQ128776773 ScholiaQ128776773MaRDI QIDQ5858997
Publication date: 15 April 2021
Published in: Optimization Methods and Software (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10556788.2018.1556660
convex optimizationpenalizationproximal algorithmadditive convex hierarchical minimizationregularized least squares problems
Numerical mathematical programming methods (65K05) Convex programming (90C25) Numerical optimization and variational techniques (65K10) Monotone operators and generalizations (47H05)
Cites Work
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- Forward-backward penalty scheme for constrained convex minimization without inf-compactness
- Coupling the gradient method with a general exterior penalization scheme for convex minimization
- Backward penalty schemes for monotone inclusion problems
- Distributed multi-agent optimization with state-dependent communication
- Incremental proximal methods for large scale convex optimization
- Gradient-type penalty method with inertial effects for solving constrained convex optimization problems with smooth data
- An inertial proximal-gradient penalization scheme for constrained convex optimization problems
- Forward-backward and Tseng's type penalty schemes for monotone inclusion problems
- Approaching the solving of constrained variational inequalities via penalty term-based dynamical systems
- Ergodic convergence to a zero of the sum of monotone operators in Hilbert space
- Gradient-free method for nonsmooth distributed optimization
- Coordinate descent algorithms
- A globally convergent incremental Newton method
- Asymptotic behavior of coupled dynamical systems with multiscale aspects
- Generalized forward-backward splitting with penalization for monotone inclusion problems
- A Generalized Forward-Backward Splitting
- Prox-Penalization and Splitting Methods for Constrained Variational Problems
- Coupling Forward-Backward with Penalty Schemes and Parallel Splitting for Constrained Variational Inequalities
- Optimization Methods for Large-Scale Machine Learning
- Signal Recovery by Proximal Forward-Backward Splitting
- Weak convergence of the sequence of successive approximations for nonexpansive mappings
- Convex analysis and monotone operator theory in Hilbert spaces