Saved in:
| Main Authors: | An, Kang, Li, Jiaxiang, Goldfarb, Donald, Ma, Shiqian |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.04418 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
ASGO: Adaptive Structured Gradient Optimization
by: An, Kang, et al.
Published: (2025)
by: An, Kang, et al.
Published: (2025)
Memory-Efficient LLM Pretraining via Minimalist Optimizer Design
by: Glentis, Athanasios, et al.
Published: (2025)
by: Glentis, Athanasios, et al.
Published: (2025)
How Does Critical Batch Size Scale in Pre-training?
by: Zhang, Hanlin, et al.
Published: (2024)
by: Zhang, Hanlin, et al.
Published: (2024)
A Riemannian ADMM
by: Li, Jiaxiang, et al.
Published: (2022)
by: Li, Jiaxiang, et al.
Published: (2022)
Online Scheduling for LLM Inference with KV Cache Constraints
by: Jaillet, Patrick, et al.
Published: (2025)
by: Jaillet, Patrick, et al.
Published: (2025)
A Queueing-Theoretic Framework for Stability Analysis of LLM Inference with KV Cache Memory Constraints
by: Nie, Chengyi, et al.
Published: (2026)
by: Nie, Chengyi, et al.
Published: (2026)
Towards Efficient Constraint Handling in Neural Solvers for Routing Problems
by: Bi, Jieyi, et al.
Published: (2026)
by: Bi, Jieyi, et al.
Published: (2026)
Weighted Low-rank Approximation via Stochastic Gradient Descent on Manifolds
by: Xu, Conglong, et al.
Published: (2025)
by: Xu, Conglong, et al.
Published: (2025)
Muon Outperforms Adam in Tail-End Associative Memory Learning
by: Wang, Shuche, et al.
Published: (2025)
by: Wang, Shuche, et al.
Published: (2025)
CLCR: Contrastive Learning-based Constraint Reordering for Efficient MILP Solving
by: Zeng, Shuli, et al.
Published: (2025)
by: Zeng, Shuli, et al.
Published: (2025)
A multilevel approach to accelerate the training of Transformers
by: Lauga, Guillaume, et al.
Published: (2025)
by: Lauga, Guillaume, et al.
Published: (2025)
The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training
by: Wang, Jinbo, et al.
Published: (2025)
by: Wang, Jinbo, et al.
Published: (2025)
Stochastic Optimization with Constraints: A Non-asymptotic Instance-Dependent Analysis
by: Khamaru, Koulik
Published: (2024)
by: Khamaru, Koulik
Published: (2024)
Efficient and provably convergent end-to-end training of deep neural networks with linear constraints
by: Yang, Zonglin, et al.
Published: (2026)
by: Yang, Zonglin, et al.
Published: (2026)
Constraint-Anchored Attribution: Feasibility-Certified Counterfactuals and Bonferroni-PAC Sufficient Subsets for Neural CO Policies
by: Lafifi, Sohaib
Published: (2026)
by: Lafifi, Sohaib
Published: (2026)
Hierarchical Deep Reinforcement Learning Framework for Multi-Year Asset Management Under Budget Constraints
by: Fard, Amir, et al.
Published: (2025)
by: Fard, Amir, et al.
Published: (2025)
T-SKM-Net: Trainable Neural Network Framework for Linear Constraint Satisfaction via Sampling Kaczmarz-Motzkin Method
by: Zhu, Haoyu, et al.
Published: (2025)
by: Zhu, Haoyu, et al.
Published: (2025)
Active Constraint Learning in High Dimensions from Demonstrations
by: Qiu, Zheng, et al.
Published: (2025)
by: Qiu, Zheng, et al.
Published: (2025)
Budget-aware Auto Optimizer Configurator
by: Liu, Kang, et al.
Published: (2026)
by: Liu, Kang, et al.
Published: (2026)
Exact Dual Geometry of SOC-ICNN Value Functions
by: Liu, Kang, et al.
Published: (2026)
by: Liu, Kang, et al.
Published: (2026)
LLM Serving Optimization with Variable Prefill and Decode Lengths
by: Wang, Meixuan, et al.
Published: (2025)
by: Wang, Meixuan, et al.
Published: (2025)
On Relatively Smooth Optimization over Riemannian Manifolds
by: He, Chang, et al.
Published: (2025)
by: He, Chang, et al.
Published: (2025)
Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling
by: Li, Sirui, et al.
Published: (2025)
by: Li, Sirui, et al.
Published: (2025)
Adaptively Robust LLM Inference Optimization under Prediction Uncertainty
by: Chen, Zixi, et al.
Published: (2025)
by: Chen, Zixi, et al.
Published: (2025)
LLM Embeddings Improve Test-time Adaptation to Tabular $Y|X$-Shifts
by: Zeng, Yibo, et al.
Published: (2024)
by: Zeng, Yibo, et al.
Published: (2024)
From Soliloquy to Agora: Memory-Enhanced LLM Agents with Decentralized Debate for Optimization Modeling
by: Lin, Jianghao, et al.
Published: (2026)
by: Lin, Jianghao, et al.
Published: (2026)
Lyapunov Function Consistent Adaptive Network Signal Control with Back Pressure and Reinforcement Learning
by: Ma, Chaolun, et al.
Published: (2022)
by: Ma, Chaolun, et al.
Published: (2022)
Quantization through Piecewise-Affine Regularization: Optimization and Statistical Guarantees
by: Ma, Jianhao, et al.
Published: (2025)
by: Ma, Jianhao, et al.
Published: (2025)
On the Optimal Construction of Unbiased Gradient Estimators for Zeroth-Order Optimization
by: Ma, Shaocong, et al.
Published: (2025)
by: Ma, Shaocong, et al.
Published: (2025)
Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets
by: Ma, Shaocong, et al.
Published: (2025)
by: Ma, Shaocong, et al.
Published: (2025)
Revisiting Zeroth-Order Optimization: Minimum-Variance Two-Point Estimators and Directionally Aligned Perturbations
by: Ma, Shaocong, et al.
Published: (2025)
by: Ma, Shaocong, et al.
Published: (2025)
OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents
by: Ao, Ruicheng, et al.
Published: (2026)
by: Ao, Ruicheng, et al.
Published: (2026)
AutoBalance: An Automatic Balancing Framework for Training Physics-Informed Neural Networks
by: An, Kang, et al.
Published: (2025)
by: An, Kang, et al.
Published: (2025)
Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach
by: Wang, Hongye, et al.
Published: (2025)
by: Wang, Hongye, et al.
Published: (2025)
Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints
by: Ao, Ruicheng, et al.
Published: (2025)
by: Ao, Ruicheng, et al.
Published: (2025)
New Hybrid Fine-Tuning Paradigm for LLMs: Algorithm Design and Convergence Analysis Framework
by: Ma, Shaocong, et al.
Published: (2026)
by: Ma, Shaocong, et al.
Published: (2026)
Differentiable Distributionally Robust Optimization Layers
by: Ma, Xutao, et al.
Published: (2024)
by: Ma, Xutao, et al.
Published: (2024)
Riemannian Dueling Optimization
by: Ren, Yuxuan, et al.
Published: (2026)
by: Ren, Yuxuan, et al.
Published: (2026)
Bregman Douglas-Rachford Splitting Method
by: Ma, Shiqian, et al.
Published: (2025)
by: Ma, Shiqian, et al.
Published: (2025)
Preconditioning Benefits of Spectral Orthogonalization in Muon
by: Ma, Jianhao, et al.
Published: (2026)
by: Ma, Jianhao, et al.
Published: (2026)
Similar Items
-
ASGO: Adaptive Structured Gradient Optimization
by: An, Kang, et al.
Published: (2025) -
Memory-Efficient LLM Pretraining via Minimalist Optimizer Design
by: Glentis, Athanasios, et al.
Published: (2025) -
How Does Critical Batch Size Scale in Pre-training?
by: Zhang, Hanlin, et al.
Published: (2024) -
A Riemannian ADMM
by: Li, Jiaxiang, et al.
Published: (2022) -
Online Scheduling for LLM Inference with KV Cache Constraints
by: Jaillet, Patrick, et al.
Published: (2025)