Mikhail A. Bragin

According to our database, Mikhail A. Bragin authored at least 30 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Survey on Lagrangian relaxation for MILP: importance, challenges, historical review, recent advancements, and opportunities.

Mikhail A. Bragin

Ann. Oper. Res., February, 2024

Near-Optimal Scheduling for IC Packaging Operations Considering Processing-Time Variations and Factory Practices.

Tsung-Han Tsai, Bing Yan, Peter B. Luh, Haw Ching Yang, Mikhail A. Bragin, Fan-Tien Cheng

IEEE Robotics Autom. Lett., 2024

2023

Surrogate Lagrangian Relaxation: A Path to Retrain-Free Deep Neural Network Pruning.

Shanglin Zhou, Mikhail A. Bragin, Deniz Gurevin, Lynn Pepin, Fei Miao, Caiwen Ding

ACM Trans. Design Autom. Electr. Syst., November, 2023

Optimize Deep Decarbonization Pathways in California with Power System Planning Using Surrogate Level-based Lagrangian Relaxation.

Osten Anderson, Nanpeng Yu, Mikhail A. Bragin

CoRR, 2023

Novel Quality Measure and Efficient Resolution of Convex Hull Pricing for Unit Commitment.

Mikhail A. Bragin, Farhan Hyder, Bing Yan, Peter B. Luh, Jinye Zhao, Feng Zhao, Dane A. Schiro, Tongxin Zheng

CoRR, 2023

Toward Efficient Transportation Electrification of Heavy-Duty Trucks: Joint Scheduling of Truck Routing and Charging.

Mikhail A. Bragin, Zuzhao Ye, Nanpeng Yu

CoRR, 2023

Lagrangian Relaxation for Mixed-Integer Linear Programming: Importance, Challenges, Recent Advancements, and Opportunities.

Mikhail A. Bragin

CoRR, 2023

An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems.

Jingtao Qin, Yuanqi Gao, Mikhail A. Bragin, Nanpeng Yu

IEEE Access, 2023

Automatic Subnetwork Search Through Dynamic Differentiable Neuron Pruning.

Zigeng Wang, Bingbing Li, Xia Xiao, Tianyun Zhang, Mikhail A. Bragin, Bing Yan, Caiwen Ding, Sanguthevar Rajasekaran

Proceedings of the 24th International Symposium on Quality Electronic Design, 2023

Towards Lossless Head Pruning through Automatic Peer Distillation for Language Models.

Bingbing Li, Zigeng Wang, Shaoyi Huang, Mikhail A. Bragin, Ji Li, Caiwen Ding

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

2022

An Innovative Formulation Tightening Approach for Job-Shop Scheduling.

Bing Yan, Mikhail A. Bragin, Peter B. Luh

IEEE Trans Autom. Sci. Eng., 2022

Combining multi-view ensemble and surrogate lagrangian relaxation for real-time 3D biomedical image segmentation on the edge.

Shanglin Zhou, Xiaowei Xu, Jun Bai, Mikhail A. Bragin

Neurocomputing, 2022

Incorporating FAT and privacy aware AI modeling approaches into business decision making frameworks.

Dmitry Zhdanov, Sudip Bhattacharjee, Mikhail A. Bragin

Decis. Support Syst., 2022

Toward Agile and Robust Supply Chains: A Lesson from Stochastic Job-Shop Scheduling.

Mikhail A. Bragin, Matthew E. Wilhelm, Matthew D. Stuber

CoRR, 2022

Surrogate "Level-Based" Lagrangian Relaxation for Mixed-Integer Linear Programming.

Mikhail A. Bragin

CoRR, 2022

2021

Distributed and Asynchronous Coordination of a Mixed-Integer Linear System via Surrogate Lagrangian Relaxation.

Mikhail A. Bragin, Bing Yan, Peter B. Luh

IEEE Trans Autom. Sci. Eng., 2021

A Novel Integer Linear Programming Formulation for Job-Shop Scheduling Problems.

Anbang Liu, Peter B. Luh, Bing Yan, Mikhail A. Bragin

IEEE Robotics Autom. Lett., 2021

Distributed and Asynchronous Operational Optimization of Networked Microgrids.

Nima Nikmehr, Mikhail A. Bragin, Peter B. Luh, Peng Zhang

CoRR, 2021

TSO-DSO Operational Planning Coordination through Surrogate Lagrangian Relaxation.

Mikhail A. Bragin, Yury Dvorkin

CoRR, 2021

Enabling Retrain-free Deep Neural Network Pruning Using Surrogate Lagrangian Relaxation.

Deniz Gurevin, Mikhail A. Bragin, Caiwen Ding, Shanglin Zhou, Lynn Pepin, Bingbing Li, Fei Miao

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

2020

Ordinal-Optimization Concept Enabled Decomposition and Coordination of Mixed-Integer Linear Programming Problems.

An-Bang Liu, Peter B. Luh, Mikhail A. Bragin, Bing Yan

IEEE Robotics Autom. Lett., 2020

A Surrogate Lagrangian Relaxation-based Model Compression for Deep Neural Networks.

Deniz Gurevin, Shanglin Zhou, Lynn Pepin, Bingbing Li, Mikhail A. Bragin, Caiwen Ding, Fei Miao

CoRR, 2020

Tightened Formulation and Resolution of Energy-Efficient Job-Shop Scheduling.

Bing Yan, Mikhail A. Bragin, Peter B. Luh

Proceedings of the 16th IEEE International Conference on Automation Science and Engineering, 2020

2019

A Scalable Solution Methodology for Mixed-Integer Linear Programming Problems Arising in Automation.

Mikhail A. Bragin, Peter B. Luh, Bing Yan, Xiaorong Sun

IEEE Trans Autom. Sci. Eng., 2019

2018

Novel Formulation and Resolution of Job-Shop Scheduling Problems.

Bing Yan, Mikhail A. Bragin, Peter B. Luh

IEEE Robotics Autom. Lett., 2018

Active Fault Management for Microgrids.

Wenfeng Wan, Yan Li, Bing Yan, Mikhail A. Bragin, Jason Philhower, Peng Zhang, Peter B. Luh, Guy Warner

Proceedings of the IECON 2018, 2018

2017

Grid integration of wind generation considering remote wind farms: hybrid markovian and interval unit commitment.

Bing Yan, Haipei Fan, Peter B. Luh, Khosrow Moslehi, Xiaoming Feng, Chien Ning Yu, Mikhail A. Bragin, Yaowen Yu

IEEE CAA J. Autom. Sinica, 2017

2016

An efficient approach for solving mixed-integer programming problems under the monotonic condition.

Mikhail A. Bragin, Peter B. Luh, Joseph H. Yan, Gary A. Stern

J. Control. Decis., 2016

2015

Convergence of the Surrogate Lagrangian Relaxation Method.

Mikhail A. Bragin, Peter B. Luh, Joseph H. Yan, Nanpeng Yu, Gary A. Stern

J. Optim. Theory Appl., 2015

2014

Energy-efficient building clusters.

Bing Yan, Peter B. Luh, Mikhail A. Bragin, Chen Song, Chenhui Dong, Zhongxue Gan

Proceedings of the 2014 IEEE International Conference on Automation Science and Engineering, 2014