Batched First-Order Methods for Parallel LP Solving in MIP
Fuente:
arXiv
Saved in:
| Main Authors: | Blin, Nicolas, Gualandi, Stefano, Maes, Christopher, Lodi, Andrea, Stellato, Bartolomeo |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Exact Verification of First-Order Methods via Mixed-Integer Linear Programming
by: Ranjan, Vinit, et al.
Published: (2024)
by: Ranjan, Vinit, et al.
Published: (2024)
Verification of First-Order Methods for Parametric Quadratic Optimization
by: Ranjan, Vinit, et al.
Published: (2024)
by: Ranjan, Vinit, et al.
Published: (2024)
From Sequential Nodes to GPU Batches: Parallel Branch and Bound for Optimal $k$-Sparse GLMs
by: Liu, Jiachang, et al.
Published: (2026)
by: Liu, Jiachang, et al.
Published: (2026)
Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization
by: Sambharya, Rajiv, et al.
Published: (2024)
by: Sambharya, Rajiv, et al.
Published: (2024)
Data-Driven Performance Guarantees for Classical and Learned Optimizers
by: Sambharya, Rajiv, et al.
Published: (2024)
by: Sambharya, Rajiv, et al.
Published: (2024)
Distributionally-Robust Learning to Optimize
by: Ranjan, Vinit, et al.
Published: (2026)
by: Ranjan, Vinit, et al.
Published: (2026)
Data-driven Analysis of First-Order Methods via Distributionally Robust Optimization
by: Park, Jisun, et al.
Published: (2025)
by: Park, Jisun, et al.
Published: (2025)
Scalable First-order Method for Certifying Optimal k-Sparse GLMs
by: Liu, Jiachang, et al.
Published: (2025)
by: Liu, Jiachang, et al.
Published: (2025)
GPU-friendly and Linearly Convergent First-order Methods for Certifying Optimal $k$-sparse GLMs
by: Liu, Jiachang, et al.
Published: (2026)
by: Liu, Jiachang, et al.
Published: (2026)
A neural network-based approach to hybrid systems identification for control
by: Fabiani, Filippo, et al.
Published: (2024)
by: Fabiani, Filippo, et al.
Published: (2024)
Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
by: Chen, Hao, et al.
Published: (2026)
by: Chen, Hao, et al.
Published: (2026)
Solving 0-1 Integer Programs with Unknown Knapsack Constraints Using Membership Oracles
by: Messana, Rosario, et al.
Published: (2024)
by: Messana, Rosario, et al.
Published: (2024)
How hard is learning to cut? Trade-offs and sample complexity
by: Khalife, Sammy, et al.
Published: (2025)
by: Khalife, Sammy, et al.
Published: (2025)
Conformal Prediction for Early Stopping in Mixed Integer Optimization
by: Clarke, Stefan, et al.
Published: (2026)
by: Clarke, Stefan, et al.
Published: (2026)
Learning-Based Hierarchical Approach for Fast Mixed-Integer Optimization
by: Clarke, Stefan, et al.
Published: (2025)
by: Clarke, Stefan, et al.
Published: (2025)
Learning to Handle Parameter Perturbations in Combinatorial Optimization: an Application to Facility Location
by: Lodi, Andrea, et al.
Published: (2019)
by: Lodi, Andrea, et al.
Published: (2019)
On the Complexity of First-Order Methods in Stochastic Bilevel Optimization
by: Kwon, Jeongyeol, et al.
Published: (2024)
by: Kwon, Jeongyeol, et al.
Published: (2024)
First-Order Methods for Linearly Constrained Bilevel Optimization
by: Kornowski, Guy, et al.
Published: (2024)
by: Kornowski, Guy, et al.
Published: (2024)
Leveraging Large Language Models for Solving Rare MIP Challenges
by: Wang, Teng, et al.
Published: (2024)
by: Wang, Teng, et al.
Published: (2024)
The Differentiable Feasibility Pump
by: Cacciola, Matteo, et al.
Published: (2024)
by: Cacciola, Matteo, et al.
Published: (2024)
Accelerated Fully First-Order Methods for Bilevel and Minimax Optimization
by: Li, Chris Junchi
Published: (2024)
by: Li, Chris Junchi
Published: (2024)
Fast Online Distributionally Robust Optimization via Data Compression
by: Wang, Irina, et al.
Published: (2025)
by: Wang, Irina, et al.
Published: (2025)
Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism
by: Lau, Tim Tsz-Kit, et al.
Published: (2024)
by: Lau, Tim Tsz-Kit, et al.
Published: (2024)
Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning
by: Sherman, Uri, et al.
Published: (2025)
by: Sherman, Uri, et al.
Published: (2025)
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
by: Beznosikov, Aleksandr, et al.
Published: (2023)
by: Beznosikov, Aleksandr, et al.
Published: (2023)
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation
by: Kwon, Jeongyeol, et al.
Published: (2023)
by: Kwon, Jeongyeol, et al.
Published: (2023)
Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming
by: Scavuzzo, Lara, et al.
Published: (2024)
by: Scavuzzo, Lara, et al.
Published: (2024)
On the Role of Batch Size in Stochastic Conditional Gradient Methods
by: Islamov, Rustem, et al.
Published: (2026)
by: Islamov, Rustem, et al.
Published: (2026)
Equitable Data-Driven Facility Location and Resource Allocation to Fight the Opioid Epidemic
by: Luo, Joyce, et al.
Published: (2023)
by: Luo, Joyce, et al.
Published: (2023)
Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural Networks
by: Bernardelli, Ambrogio Maria, et al.
Published: (2022)
by: Bernardelli, Ambrogio Maria, et al.
Published: (2022)
First and Second Order Approximations to Stochastic Gradient Descent Methods with Momentum Terms
by: Lu, Eric
Published: (2025)
by: Lu, Eric
Published: (2025)
Who Plays First? Optimizing the Order of Play in Stackelberg Games with Many Robots
by: Hu, Haimin, et al.
Published: (2024)
by: Hu, Haimin, et al.
Published: (2024)
SMiLE: Provably Enforcing Global Relational Properties in Neural Networks
by: Francobaldi, Matteo, et al.
Published: (2025)
by: Francobaldi, Matteo, et al.
Published: (2025)
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles
by: Scaman, Kevin, et al.
Published: (2023)
by: Scaman, Kevin, et al.
Published: (2023)
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning
by: Hu, Yifan, et al.
Published: (2020)
by: Hu, Yifan, et al.
Published: (2020)
Learning Decision-Focused Uncertainty Sets in Robust Optimization
by: Wang, Irina, et al.
Published: (2023)
by: Wang, Irina, et al.
Published: (2023)
Single- vs. Dual-Policy Reinforcement Learning for Dynamic Bike Rebalancing
by: Liang, Jiaqi, et al.
Published: (2024)
by: Liang, Jiaqi, et al.
Published: (2024)
Penalty-Based First-Order Methods for Bilevel Optimization with Minimax and Constrained Lower-Level Problems
by: Shen, Yiyang, et al.
Published: (2026)
by: Shen, Yiyang, et al.
Published: (2026)
Optimal Local Convergence Rates of Stochastic First-Order Methods under Local $α$-PL
by: Masiha, Saeed, et al.
Published: (2024)
by: Masiha, Saeed, et al.
Published: (2024)
First-ish Order Methods: Hessian-aware Scalings of Gradient Descent
by: Smee, Oscar, et al.
Published: (2025)
by: Smee, Oscar, et al.
Published: (2025)
Similar Items
-
Exact Verification of First-Order Methods via Mixed-Integer Linear Programming
by: Ranjan, Vinit, et al.
Published: (2024) -
Verification of First-Order Methods for Parametric Quadratic Optimization
by: Ranjan, Vinit, et al.
Published: (2024) -
From Sequential Nodes to GPU Batches: Parallel Branch and Bound for Optimal $k$-Sparse GLMs
by: Liu, Jiachang, et al.
Published: (2026) -
Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization
by: Sambharya, Rajiv, et al.
Published: (2024) -
Data-Driven Performance Guarantees for Classical and Learned Optimizers
by: Sambharya, Rajiv, et al.
Published: (2024)