Incremental proximal gradient scheme with penalization for constrained composite convex optimization problems
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Publication:4999758
DOI10.1080/02331934.2020.1846188zbMath1481.65090arXiv1909.05060OpenAlexW3108243313MaRDI QIDQ4999758
Publication date: 2 July 2021
Published in: Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1909.05060
convex optimizationpenalizationFenchel conjugateproximal gradient algorithmincremental proximal method
Numerical mathematical programming methods (65K05) Convex programming (90C25) Numerical optimization and variational techniques (65K10)
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Cites Work
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- Inertial Douglas-Rachford splitting for monotone inclusion problems
- 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
- Minimizing finite sums with the stochastic average gradient
- 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
- Asymptotic behavior of coupled dynamical systems with multiscale aspects
- Generalized forward-backward splitting with penalization for monotone inclusion problems
- A new class of distributed optimization algorithms: application to regression of distributed data
- Solving a Generalized Heron Problem by Means of Convex Analysis
- Prox-Penalization and Splitting Methods for Constrained Variational Problems
- Applications of variational analysis to a generalized Heron problem
- Coupling Forward-Backward with Penalty Schemes and Parallel Splitting for Constrained Variational Inequalities
- Optimization Methods for Large-Scale Machine Learning
- Convergence of Approximate and Incremental Subgradient Methods for Convex Optimization
- Are Loss Functions All the Same?
- Distributed Subgradient Methods for Multi-Agent Optimization
- A forward–backward penalty scheme with inertial effects for monotone inclusions. Applications to convex bilevel programming
- A Convergent Incremental Gradient Method with a Constant Step Size
- Convex analysis and monotone operator theory in Hilbert spaces
- Sparse Image and Signal Processing
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