Sample-wise Constrained Learning via a Sequential Penalty Approach with Applications in Image Processing
Fuente:
arXiv
Saved in:
| Main Authors: | Lanzillotta, Francesca, Albisani, Chiara, Pucci, Davide, Baracchi, Daniele, Piva, Alessandro, Lapucci, Matteo |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems
by: Lapucci, Matteo, et al.
Published: (2024)
by: Lapucci, Matteo, et al.
Published: (2024)
Convergence Conditions for Stochastic Line Search Based Optimization of Over-parametrized Models
by: Lapucci, Matteo, et al.
Published: (2024)
by: Lapucci, Matteo, et al.
Published: (2024)
Representation and Regression Problems in Neural Networks: Relaxation, Generalization, and Numerics
by: Liu, Kang, et al.
Published: (2024)
by: Liu, Kang, et al.
Published: (2024)
Moments, Time-Inversion and Source Identification for the Heat Equation
by: Liu, Kang, et al.
Published: (2025)
by: Liu, Kang, et al.
Published: (2025)
How a Small Amount of Data Sharing Benefits Distributed Optimization and Learning : The Upside of Data Heterogeneity
by: Zhu, Mingxi, et al.
Published: (2022)
by: Zhu, Mingxi, et al.
Published: (2022)
Distributed Computing for Huge-Scale Aggregative Convex Programming
by: Tao, Luoyi
Published: (2026)
by: Tao, Luoyi
Published: (2026)
Penalty decomposition derivative free method for the minimization of partially separable functions over a convex feasible set
by: Cecere, Francesco, et al.
Published: (2025)
by: Cecere, Francesco, et al.
Published: (2025)
A Two-Phase Adaptive Balanced Penalty Method for Controllable Pareto Front Learning under Split Feasibility Conditions
by: Hoang, Nguyen Viet, et al.
Published: (2026)
by: Hoang, Nguyen Viet, et al.
Published: (2026)
Faster Adaptive Optimization via Expected Gradient Outer Product Reparameterization
by: DePavia, Adela, et al.
Published: (2025)
by: DePavia, Adela, et al.
Published: (2025)
Stable gradient-adjusted root mean square propagation on least squares problem
by: Li, Runze, et al.
Published: (2024)
by: Li, Runze, et al.
Published: (2024)
Accelerating preconditioned ADMM via degenerate proximal point mappings
by: Sun, Defeng, et al.
Published: (2024)
by: Sun, Defeng, et al.
Published: (2024)
Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications
by: Lee, Youngkyu, et al.
Published: (2024)
by: Lee, Youngkyu, et al.
Published: (2024)
Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies
by: Kopaničáková, Alena, et al.
Published: (2023)
by: Kopaničáková, Alena, et al.
Published: (2023)
Classification by Separating Hypersurfaces: An Entropic Approach
by: Arratia, Argimiro, et al.
Published: (2025)
by: Arratia, Argimiro, et al.
Published: (2025)
Effective Front-Descent Algorithms with Convergence Guarantees
by: Lapucci, Matteo, et al.
Published: (2024)
by: Lapucci, Matteo, et al.
Published: (2024)
Incremental Certificate Learning for Hybrid Neural Network Verification . A Solver Architecture for Piecewise-Linear Safety Queries
by: Gokavarapu, Chandrasekhar
Published: (2025)
by: Gokavarapu, Chandrasekhar
Published: (2025)
Stochastic versus Deterministic in Stochastic Gradient Descent
by: Li, Runze, et al.
Published: (2025)
by: Li, Runze, et al.
Published: (2025)
Mixed-Integer Linear Optimization for Cardinality-Constrained Random Forests
by: Burgard, Jan Pablo, et al.
Published: (2024)
by: Burgard, Jan Pablo, et al.
Published: (2024)
Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing
by: Yi, Zeji, et al.
Published: (2026)
by: Yi, Zeji, et al.
Published: (2026)
BROS: Bias-Corrected Randomized Subspaces for Memory-Efficient Single-Loop Bilevel Optimization
by: Zhang, Hengrui, et al.
Published: (2026)
by: Zhang, Hengrui, et al.
Published: (2026)
A Heuristic Alternating Direction Method of Multipliers Framework for Distributed and Centralized Tree-Constrained Optimization: Applications to Hop-Constrained Spanning Tree Multicommodity Flow Design
by: Mokhtari, Yacine
Published: (2025)
by: Mokhtari, Yacine
Published: (2025)
An Augmented Lagrangian Method for Training Recurrent Neural Networks
by: Wang, Yue, et al.
Published: (2024)
by: Wang, Yue, et al.
Published: (2024)
A Globally Convergent Gradient Method with Momentum
by: Lapucci, Matteo, et al.
Published: (2024)
by: Lapucci, Matteo, et al.
Published: (2024)
Developing heuristic solution techniques for large-scale unit commitment models
by: Kempke, Nils-Christian, et al.
Published: (2025)
by: Kempke, Nils-Christian, et al.
Published: (2025)
Cardinality-Constrained Multi-Objective Optimization: Novel Optimality Conditions and Algorithms
by: Lapucci, Matteo, et al.
Published: (2023)
by: Lapucci, Matteo, et al.
Published: (2023)
Recovery of Integer Images from Minimal DFT Measurements: Uniqueness and Inversion Algorithms
by: Levinson, Howard W, et al.
Published: (2025)
by: Levinson, Howard W, et al.
Published: (2025)
CRAFT: Conflict-Resolved Aggregation for Federated Training
by: Wang, Ziqi, et al.
Published: (2026)
by: Wang, Ziqi, et al.
Published: (2026)
Mixed-Integer Linear Optimization for Semi-Supervised Optimal Classification Trees
by: Burgard, Jan Pablo, et al.
Published: (2024)
by: Burgard, Jan Pablo, et al.
Published: (2024)
A Matrix Optimization Method for Blind Extraction of External Equitable Partitions from Low Pass Graph Signals
by: Teng, Wenshun, et al.
Published: (2025)
by: Teng, Wenshun, et al.
Published: (2025)
Distributed Computing for Huge-Scale Linear Programming
by: Tao, Luoyi
Published: (2024)
by: Tao, Luoyi
Published: (2024)
Preconditioned subgradient method for composite optimization: overparameterization and fast convergence
by: Díaz, Mateo, et al.
Published: (2025)
by: Díaz, Mateo, et al.
Published: (2025)
Optimization with Trained Machine Learning Models Embedded
by: Schweidtmann, Artur M., et al.
Published: (2022)
by: Schweidtmann, Artur M., et al.
Published: (2022)
Minimizing Maximum Dissatisfaction in the Allocation of Indivisible Items under a Common Preference Graph
by: Chiarelli, Nina, et al.
Published: (2023)
by: Chiarelli, Nina, et al.
Published: (2023)
A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization
by: Ustaomeroglu, Muhammed, et al.
Published: (2025)
by: Ustaomeroglu, Muhammed, et al.
Published: (2025)
Performance Estimation of second-order optimization methods on classes of univariate functions
by: Rubbens, Anne, et al.
Published: (2025)
by: Rubbens, Anne, et al.
Published: (2025)
Greedy and randomized heuristics for optimization of k-domination models in digraphs and road networks
by: Dijkstra, Lukas, et al.
Published: (2024)
by: Dijkstra, Lukas, et al.
Published: (2024)
On the Computation of the Efficient Frontier in Advanced Sparse Portfolio Optimization
by: Annunziata, Arturo, et al.
Published: (2025)
by: Annunziata, Arturo, et al.
Published: (2025)
Word-Representability of Graphs with respect to Split Recomposition
by: Dwary, Tithi, et al.
Published: (2024)
by: Dwary, Tithi, et al.
Published: (2024)
Fitted value iteration methods for bicausal optimal transport
by: Bayraktar, Erhan, et al.
Published: (2023)
by: Bayraktar, Erhan, et al.
Published: (2023)
Towards Solving Polynomial-Objective Integer Programming with Hypergraph Neural Networks
by: Li, Minshuo, et al.
Published: (2026)
by: Li, Minshuo, et al.
Published: (2026)
Similar Items
-
Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems
by: Lapucci, Matteo, et al.
Published: (2024) -
Convergence Conditions for Stochastic Line Search Based Optimization of Over-parametrized Models
by: Lapucci, Matteo, et al.
Published: (2024) -
Representation and Regression Problems in Neural Networks: Relaxation, Generalization, and Numerics
by: Liu, Kang, et al.
Published: (2024) -
Moments, Time-Inversion and Source Identification for the Heat Equation
by: Liu, Kang, et al.
Published: (2025) -
How a Small Amount of Data Sharing Benefits Distributed Optimization and Learning : The Upside of Data Heterogeneity
by: Zhu, Mingxi, et al.
Published: (2022)