Saved in:
| Main Authors: | Er, Guner Dilsad, Trimpe, Sebastian, Muehlebach, Michael |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.10618 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Systems-Theoretic View on the Convergence of Algorithms under Disturbances
by: Er, Guner Dilsad, et al.
Published: (2025)
by: Er, Guner Dilsad, et al.
Published: (2025)
Controlling Participation in Federated Learning with Feedback
by: Cummins, Michael, et al.
Published: (2024)
by: Cummins, Michael, et al.
Published: (2024)
Teaching Machine Learning Fundamentals with LEGO Robotics
by: Sydora, Viacheslav, et al.
Published: (2026)
by: Sydora, Viacheslav, et al.
Published: (2026)
Decision-Dependent Stochastic Optimization: The Role of Distribution Dynamics
by: He, Zhiyu, et al.
Published: (2025)
by: He, Zhiyu, et al.
Published: (2025)
A Pontryagin Perspective on Reinforcement Learning
by: Eberhard, Onno, et al.
Published: (2024)
by: Eberhard, Onno, et al.
Published: (2024)
The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective
by: Muehlebach, Michael, et al.
Published: (2025)
by: Muehlebach, Michael, et al.
Published: (2025)
On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems
by: Muehlebach, Michael, et al.
Published: (2021)
by: Muehlebach, Michael, et al.
Published: (2021)
Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining
by: Hose, Henrik, et al.
Published: (2024)
by: Hose, Henrik, et al.
Published: (2024)
Primal Methods for Variational Inequality Problems with Functional Constraints
by: Zhang, Liang, et al.
Published: (2024)
by: Zhang, Liang, et al.
Published: (2024)
A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events
by: Taboga, Vincent, et al.
Published: (2023)
by: Taboga, Vincent, et al.
Published: (2023)
Accelerated First-Order Optimization under Nonlinear Constraints
by: Muehlebach, Michael, et al.
Published: (2023)
by: Muehlebach, Michael, et al.
Published: (2023)
Approximate non-linear model predictive control with safety-augmented neural networks
by: Hose, Henrik, et al.
Published: (2023)
by: Hose, Henrik, et al.
Published: (2023)
Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM
by: Noah, Yoav, et al.
Published: (2023)
by: Noah, Yoav, et al.
Published: (2023)
Automatic nonlinear MPC approximation with closed-loop guarantees
by: Tokmak, Abdullah, et al.
Published: (2023)
by: Tokmak, Abdullah, et al.
Published: (2023)
CoCoA Is ADMM: Unifying Two Paradigms in Distributed Optimization
by: Wu, Runxiong, et al.
Published: (2025)
by: Wu, Runxiong, et al.
Published: (2025)
Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
by: Lin, Junan, et al.
Published: (2026)
by: Lin, Junan, et al.
Published: (2026)
Learning to accelerate distributed ADMM using graph neural networks
by: Doerks, Henri, et al.
Published: (2025)
by: Doerks, Henri, et al.
Published: (2025)
Joint Cooperative and Non-Cooperative Localization in WSNs with Distributed Scaled Proximal ADMM Algorithms
by: Zhu, Qiaojia, et al.
Published: (2025)
by: Zhu, Qiaojia, et al.
Published: (2025)
Towards a Systems Theory of Algorithms
by: Dörfler, Florian, et al.
Published: (2024)
by: Dörfler, Florian, et al.
Published: (2024)
Federated ADMM from Bayesian Duality
by: Möllenhoff, Thomas, et al.
Published: (2025)
by: Möllenhoff, Thomas, et al.
Published: (2025)
Federated Learning Using Three-Operator ADMM
by: Kant, Shashi, et al.
Published: (2022)
by: Kant, Shashi, et al.
Published: (2022)
HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
by: Tran, Trinh, et al.
Published: (2026)
by: Tran, Trinh, et al.
Published: (2026)
The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach
by: Song, Yongcun, et al.
Published: (2023)
by: Song, Yongcun, et al.
Published: (2023)
ADMM for Structured Fractional Minimization
by: Yuan, Ganzhao
Published: (2024)
by: Yuan, Ganzhao
Published: (2024)
A General Continuous-Time Formulation of Stochastic ADMM and Its Variants
by: Li, Chris Junchi
Published: (2024)
by: Li, Chris Junchi
Published: (2024)
ADMM Algorithms for Residual Network Training: Convergence Analysis and Parallel Implementation
by: Xu, Jintao, et al.
Published: (2023)
by: Xu, Jintao, et al.
Published: (2023)
A Riemannian ADMM
by: Li, Jiaxiang, et al.
Published: (2022)
by: Li, Jiaxiang, et al.
Published: (2022)
Zeroth-Order Optimization at the Edge of Stability
by: Song, Minhak, et al.
Published: (2026)
by: Song, Minhak, et al.
Published: (2026)
Zeroth-Order Optimization Finds Flat Minima
by: Zhang, Liang, et al.
Published: (2025)
by: Zhang, Liang, et al.
Published: (2025)
Residual-Evasive Attacks on ADMM in Distributed Optimization
by: Bruckmeier, Sabrina, et al.
Published: (2025)
by: Bruckmeier, Sabrina, et al.
Published: (2025)
FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning
by: Song, Yongcun, et al.
Published: (2024)
by: Song, Yongcun, et al.
Published: (2024)
Decision-Focused Bias Correction for Fluid Approximation
by: Er, Can, et al.
Published: (2025)
by: Er, Can, et al.
Published: (2025)
A Fair OR-ML Framework for Resource Substitution in Large-Scale Networks
by: Mohan, Ved, et al.
Published: (2025)
by: Mohan, Ved, et al.
Published: (2025)
A Rolling-Space Branch-and-Price Algorithm for the Multi-Compartment Vehicle Routing Problem with Multiple Time Windows
by: Raqabi, El Mehdi Er, et al.
Published: (2026)
by: Raqabi, El Mehdi Er, et al.
Published: (2026)
Feature-Based Interpretable Surrogates for Optimization
by: Goerigk, Marc, et al.
Published: (2024)
by: Goerigk, Marc, et al.
Published: (2024)
Distributionally Robust Federated Learning with Outlier Resilience
by: Wang, Zifan, et al.
Published: (2025)
by: Wang, Zifan, et al.
Published: (2025)
Accelerated Distributed Optimization with Compression and Error Feedback
by: Gao, Yuan, et al.
Published: (2025)
by: Gao, Yuan, et al.
Published: (2025)
Scalable DC Optimization via Adaptive Frank-Wolfe Algorithms
by: Pokutta, Sebastian
Published: (2025)
by: Pokutta, Sebastian
Published: (2025)
ADMM-Based Training for Spiking Neural Networks
by: Perin, Giovanni, et al.
Published: (2025)
by: Perin, Giovanni, et al.
Published: (2025)
When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize
by: Wang, Yi, et al.
Published: (2026)
by: Wang, Yi, et al.
Published: (2026)
Similar Items
-
A Systems-Theoretic View on the Convergence of Algorithms under Disturbances
by: Er, Guner Dilsad, et al.
Published: (2025) -
Controlling Participation in Federated Learning with Feedback
by: Cummins, Michael, et al.
Published: (2024) -
Teaching Machine Learning Fundamentals with LEGO Robotics
by: Sydora, Viacheslav, et al.
Published: (2026) -
Decision-Dependent Stochastic Optimization: The Role of Distribution Dynamics
by: He, Zhiyu, et al.
Published: (2025) -
A Pontryagin Perspective on Reinforcement Learning
by: Eberhard, Onno, et al.
Published: (2024)