Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | Kotary, James, Christopher, Jacob, Dinh, My H, Fioretto, Ferdinando |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Learning Constrained Optimization with Deep Augmented Lagrangian Methods
by: Kotary, James, et al.
Published: (2024)
by: Kotary, James, et al.
Published: (2024)
Learning to Solve Constrained Bilevel Control Co-Design Problems
by: Kotary, James, et al.
Published: (2025)
by: Kotary, James, et al.
Published: (2025)
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages
by: Dinh, My H., et al.
Published: (2024)
by: Dinh, My H., et al.
Published: (2024)
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
by: Mandi, Jayanta, et al.
Published: (2023)
by: Mandi, Jayanta, et al.
Published: (2023)
Learning to Solve Optimization Problems Constrained with Partial Differential Equations
by: Guven, Yusuf, et al.
Published: (2025)
by: Guven, Yusuf, et al.
Published: (2025)
On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passes
by: Kong, Boao, et al.
Published: (2026)
by: Kong, Boao, et al.
Published: (2026)
End-to-End Optimization and Learning of Fair Court Schedules
by: Dinh, My H, et al.
Published: (2024)
by: Dinh, My H, et al.
Published: (2024)
End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty
by: Dinh, My H, et al.
Published: (2024)
by: Dinh, My H, et al.
Published: (2024)
Understanding the Curse of Unrolling
by: Mehmood, Sheheryar, et al.
Published: (2026)
by: Mehmood, Sheheryar, et al.
Published: (2026)
HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
by: Tran, Trinh, et al.
Published: (2026)
by: Tran, Trinh, et al.
Published: (2026)
PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming
by: Li, Bingheng, et al.
Published: (2024)
by: Li, Bingheng, et al.
Published: (2024)
Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming
by: King, Ethan, et al.
Published: (2024)
by: King, Ethan, et al.
Published: (2024)
An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrolling
by: Yang, Linxin, et al.
Published: (2024)
by: Yang, Linxin, et al.
Published: (2024)
A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD
by: Jin, Ruinan, et al.
Published: (2024)
by: Jin, Ruinan, et al.
Published: (2024)
Shuffling Momentum Gradient Algorithm for Convex Optimization
by: Tran, Trang H., et al.
Published: (2024)
by: Tran, Trang H., et al.
Published: (2024)
Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization
by: Tran-Dinh, Quoc, et al.
Published: (2024)
by: Tran-Dinh, Quoc, et al.
Published: (2024)
You Shall Pass: Dealing with the Zero-Gradient Problem in Predict and Optimize for Convex Optimization
by: Veviurko, Grigorii, et al.
Published: (2023)
by: Veviurko, Grigorii, et al.
Published: (2023)
Improved Convergence Rates of Muon Optimizer for Nonconvex Optimization
by: Nagashima, Shuntaro, et al.
Published: (2026)
by: Nagashima, Shuntaro, et al.
Published: (2026)
Integration Matters for Learning PDEs with Backward SDEs
by: Park, Sungje, et al.
Published: (2025)
by: Park, Sungje, et al.
Published: (2025)
Rapid Overfitting of Multi-Pass Stochastic Gradient Descent in Stochastic Convex Optimization
by: Vansover-Hager, Shira, et al.
Published: (2025)
by: Vansover-Hager, Shira, et al.
Published: (2025)
Convergence of Some Convex Message Passing Algorithms to a Fixed Point
by: Voracek, Vaclav, et al.
Published: (2024)
by: Voracek, Vaclav, et al.
Published: (2024)
Efficient Gradient-Based Optimization for Joint Layout Design and Control of Wind Turbines
by: Kotary, James, et al.
Published: (2025)
by: Kotary, James, et al.
Published: (2025)
Convergence of Distributed Adaptive Optimization with Local Updates
by: Cheng, Ziheng, et al.
Published: (2024)
by: Cheng, Ziheng, et al.
Published: (2024)
Convergence of Spectral Descent for Non-smooth Optimization
by: Yang, Yixuan, et al.
Published: (2026)
by: Yang, Yixuan, et al.
Published: (2026)
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
by: Zhou, Dongruo, et al.
Published: (2018)
by: Zhou, Dongruo, et al.
Published: (2018)
Learning Joint Models of Prediction and Optimization
by: Kotary, James, et al.
Published: (2024)
by: Kotary, James, et al.
Published: (2024)
Improving Generalization and Convergence by Enhancing Implicit Regularization
by: Wang, Mingze, et al.
Published: (2024)
by: Wang, Mingze, et al.
Published: (2024)
Deep Backward and Galerkin Methods for the Finite State Master Equation
by: Cohen, Asaf, et al.
Published: (2024)
by: Cohen, Asaf, et al.
Published: (2024)
Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees
by: Xiao, Nachuan, et al.
Published: (2023)
by: Xiao, Nachuan, et al.
Published: (2023)
Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees
by: Deng, Yuyang, et al.
Published: (2024)
by: Deng, Yuyang, et al.
Published: (2024)
Convergence Analysis of the Lion Optimizer in Centralized and Distributed Settings
by: Jiang, Wei, et al.
Published: (2025)
by: Jiang, Wei, et al.
Published: (2025)
On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions
by: Hong, Yusu, et al.
Published: (2024)
by: Hong, Yusu, et al.
Published: (2024)
Subspace Optimization for Large Language Models with Convergence Guarantees
by: He, Yutong, et al.
Published: (2024)
by: He, Yutong, et al.
Published: (2024)
Local Linear Convergence of Infeasible Optimization with Orthogonal Constraints
by: Sun, Youbang, et al.
Published: (2024)
by: Sun, Youbang, et al.
Published: (2024)
A Unified Framework for Analyzing Meta-algorithms in Online Convex Optimization
by: Pedramfar, Mohammad, et al.
Published: (2024)
by: Pedramfar, Mohammad, et al.
Published: (2024)
Adaptive Algorithms with Sharp Convergence Rates for Stochastic Hierarchical Optimization
by: Gong, Xiaochuan, et al.
Published: (2025)
by: Gong, Xiaochuan, et al.
Published: (2025)
Unified Convergence Analysis for Adaptive Optimization with Moving Average Estimator
by: Guo, Zhishuai, et al.
Published: (2021)
by: Guo, Zhishuai, et al.
Published: (2021)
Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization
by: Zhang, Fangzhao, et al.
Published: (2024)
by: Zhang, Fangzhao, et al.
Published: (2024)
Towards Universal Convergence of Backward Error in Linear System Solvers
by: Dereziński, Michał, et al.
Published: (2026)
by: Dereziński, Michał, et al.
Published: (2026)
Unbiased and Biased Variance-Reduced Forward-Reflected-Backward Splitting Methods for Stochastic Composite Inclusions
by: Tran-Dinh, Quoc, et al.
Published: (2026)
by: Tran-Dinh, Quoc, et al.
Published: (2026)
Similar Items
-
Learning Constrained Optimization with Deep Augmented Lagrangian Methods
by: Kotary, James, et al.
Published: (2024) -
Learning to Solve Constrained Bilevel Control Co-Design Problems
by: Kotary, James, et al.
Published: (2025) -
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages
by: Dinh, My H., et al.
Published: (2024) -
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
by: Mandi, Jayanta, et al.
Published: (2023) -
Learning to Solve Optimization Problems Constrained with Partial Differential Equations
by: Guven, Yusuf, et al.
Published: (2025)