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Mikhail A. Bragin
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2020 – today
- 2024
- [j16]Mikhail A. Bragin
:
Survey on Lagrangian relaxation for MILP: importance, challenges, historical review, recent advancements, and opportunities. Ann. Oper. Res. 333(1): 29-45 (2024) - [j15]Tsung-Han Tsai
Near-Optimal Scheduling for IC Packaging Operations Considering Processing-Time Variations and Factory Practices. IEEE Robotics Autom. Lett. 9(4): 3878-3885 (2024) - [j14]Anbang Liu
Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling. IEEE Trans Autom. Sci. Eng. 21(3): 4829-4850 (2024) - [i12]Chaofan Lin, Peng Zhang, Mikhail A. Bragin, Yacov A. Shamash:
Reforming Quantum Microgrid Formation. CoRR abs/2406.05916 (2024) - 2023
- [j13]Jingtao Qin
An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems. IEEE Access 11: 100125-100136 (2023) - [j12]Shanglin Zhou
Surrogate Lagrangian Relaxation: A Path to Retrain-Free Deep Neural Network Pruning. ACM Trans. Design Autom. Electr. Syst. 28(6): 102:1-102:19 (2023) - [c6]Bingbing Li, Zigeng Wang, Shaoyi Huang, Mikhail A. Bragin, Ji Li, Caiwen Ding:
Towards Lossless Head Pruning through Automatic Peer Distillation for Language Models. IJCAI 2023: 5113-5121 - [c5]Zigeng Wang, Bingbing Li, Xia Xiao, Tianyun Zhang, Mikhail A. Bragin, Bing Yan, Caiwen Ding, Sanguthevar Rajasekaran:
Automatic Subnetwork Search Through Dynamic Differentiable Neuron Pruning. ISQED 2023: 1-6 - [i11]Mikhail A. Bragin:
Lagrangian Relaxation for Mixed-Integer Linear Programming: Importance, Challenges, Recent Advancements, and Opportunities. CoRR abs/2301.00573 (2023) - [i10]Mikhail A. Bragin, Zuzhao Ye, Nanpeng Yu:
Toward Efficient Transportation Electrification of Heavy-Duty Trucks: Joint Scheduling of Truck Routing and Charging. CoRR abs/2302.00240 (2023) - [i9]Shanglin Zhou, Mikhail A. Bragin, Lynn Pepin, Deniz Gurevin, Fei Miao, Caiwen Ding:
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning. CoRR abs/2304.04120 (2023) - [i8]Mikhail A. Bragin, Farhan Hyder, Bing Yan, Peter B. Luh, Jinye Zhao, Feng Zhao, Dane A. Schiro, Tongxin Zheng:
Novel Quality Measure and Efficient Resolution of Convex Hull Pricing for Unit Commitment. CoRR abs/2304.07990 (2023) - [i7]Osten Anderson, Nanpeng Yu, Mikhail A. Bragin:
Optimize Deep Decarbonization Pathways in California with Power System Planning Using Surrogate Level-based Lagrangian Relaxation. CoRR abs/2309.07202 (2023) - 2022
- [j11]Dmitry Zhdanov
Incorporating FAT and privacy aware AI modeling approaches into business decision making frameworks. Decis. Support Syst. 155: 113715 (2022) - [j10]Shanglin Zhou, Xiaowei Xu, Jun Bai, Mikhail A. Bragin:
Combining multi-view ensemble and surrogate lagrangian relaxation for real-time 3D biomedical image segmentation on the edge. Neurocomputing 512: 466-481 (2022) - [j9]Bing Yan
An Innovative Formulation Tightening Approach for Job-Shop Scheduling. IEEE Trans Autom. Sci. Eng. 19(3): 2526-2539 (2022) - [i6]Mikhail A. Bragin:
Surrogate "Level-Based" Lagrangian Relaxation for Mixed-Integer Linear Programming. CoRR abs/2203.04514 (2022) - [i5]Jingtao Qin, Yuanqi Gao, Mikhail A. Bragin, Nanpeng Yu:
An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems. CoRR abs/2206.04249 (2022) - [i4]Mikhail A. Bragin, Matthew E. Wilhelm, Matthew D. Stuber:
Toward Agile and Robust Supply Chains: A Lesson from Stochastic Job-Shop Scheduling. CoRR abs/2206.09326 (2022) - 2021
- [j8]Anbang Liu
A Novel Integer Linear Programming Formulation for Job-Shop Scheduling Problems. IEEE Robotics Autom. Lett. 6(3): 5937-5944 (2021) - [j7]Mikhail A. Bragin
Distributed and Asynchronous Coordination of a Mixed-Integer Linear System via Surrogate Lagrangian Relaxation. IEEE Trans Autom. Sci. Eng. 18(3): 1191-1205 (2021) - [c4]Deniz Gurevin, Mikhail A. Bragin, Caiwen Ding, Shanglin Zhou, Lynn Pepin, Bingbing Li, Fei Miao:
Enabling Retrain-free Deep Neural Network Pruning Using Surrogate Lagrangian Relaxation. IJCAI 2021: 2497-2504 - [i3]Mikhail A. Bragin, Yury Dvorkin:
TSO-DSO Operational Planning Coordination through Surrogate Lagrangian Relaxation. CoRR abs/2101.09877 (2021) - [i2]Nima Nikmehr, Mikhail A. Bragin, Peter B. Luh, Peng Zhang:
Distributed and Asynchronous Operational Optimization of Networked Microgrids. CoRR abs/2102.03496 (2021) - 2020
- [j6]An-Bang Liu
Ordinal-Optimization Concept Enabled Decomposition and Coordination of Mixed-Integer Linear Programming Problems. IEEE Robotics Autom. Lett. 5(4): 5051-5058 (2020) - [c3]Bing Yan, Mikhail A. Bragin, Peter B. Luh:
Tightened Formulation and Resolution of Energy-Efficient Job-Shop Scheduling. CASE 2020: 741-746 - [i1]Deniz Gurevin, Shanglin Zhou, Lynn Pepin, Bingbing Li, Mikhail A. Bragin, Caiwen Ding, Fei Miao:
A Surrogate Lagrangian Relaxation-based Model Compression for Deep Neural Networks. CoRR abs/2012.10079 (2020)
2010 – 2019
- 2019
- [j5]Mikhail A. Bragin
A Scalable Solution Methodology for Mixed-Integer Linear Programming Problems Arising in Automation. IEEE Trans Autom. Sci. Eng. 16(2): 531-541 (2019) - 2018
- [j4]Bing Yan
Novel Formulation and Resolution of Job-Shop Scheduling Problems. IEEE Robotics Autom. Lett. 3(4): 3387-3393 (2018) - [c2]Wenfeng Wan, Yan Li, Bing Yan, Mikhail A. Bragin
Active Fault Management for Microgrids. IECON 2018: 3815-3820 - 2017
- [j3]Bing Yan, Haipei Fan, Peter B. Luh, Khosrow Moslehi, Xiaoming Feng, Chien Ning Yu, Mikhail A. Bragin
Grid integration of wind generation considering remote wind farms: hybrid markovian and interval unit commitment. IEEE CAA J. Autom. Sinica 4(2): 205-215 (2017) - 2016
- [j2]Mikhail A. Bragin
An efficient approach for solving mixed-integer programming problems under the monotonic condition. J. Control. Decis. 3(1): 44-67 (2016) - 2015
- [j1]Mikhail A. Bragin
Convergence of the Surrogate Lagrangian Relaxation Method. J. Optim. Theory Appl. 164(1): 173-201 (2015) - 2014
- [c1]Bing Yan, Peter B. Luh, Mikhail A. Bragin
Energy-efficient building clusters. CASE 2014: 966-971
Coauthor Index
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