Energy-efficient building clusters. - Bing Yan; Peter B. Luh; Mikhail A. Bragin; Chen Song; Chenhui Dong; Zhongxue Gan (CASE, 2014)

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)

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)

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)

Active Fault Management for Microgrids. - Wenfeng Wan; Yan Li 015; Bing Yan; Mikhail A. Bragin; Jason Philhower; Peng Zhang 015; Peter B. Luh; Guy Warner (IECON, 2018)

Novel Formulation and Resolution of Job-Shop Scheduling Problems. - Bing Yan; Mikhail A. Bragin; Peter B. Luh (IEEE Robotics Autom. Lett., 2018)

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)

Tightened Formulation and Resolution of Energy-Efficient Job-Shop Scheduling. - Bing Yan; Mikhail A. Bragin; Peter B. Luh (CASE, 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)

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)

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 (IJCAI, 2021)

TSO-DSO Operational Planning Coordination through Surrogate Lagrangian Relaxation. - Mikhail A. Bragin; Yury Dvorkin (CoRR, 2021)

Distributed and Asynchronous Operational Optimization of Networked Microgrids. - Nima Nikmehr; Mikhail A. Bragin; Peter B. Luh; Peng Zhang 015 (CoRR, 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 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)

Surrogate "Level-Based" Lagrangian Relaxation for Mixed-Integer Linear Programming. - Mikhail A. Bragin (CoRR, 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)

Incorporating FAT and privacy aware AI modeling approaches into business decision making frameworks. - Dmitry Zhdanov; Sudip Bhattacharjee; Mikhail A. Bragin (Decis. Support Syst., 2022)

Combining multi-view ensemble and surrogate lagrangian relaxation for real-time 3D biomedical image segmentation on the edge. - Shanglin Zhou; Xiaowei Xu 004; Jun Bai; Mikhail A. Bragin (Neurocomputing, 2022)

An Innovative Formulation Tightening Approach for Job-Shop Scheduling. - Bing Yan; Mikhail A. Bragin; Peter B. Luh (IEEE Trans Autom. Sci. Eng., 2022)

Towards Lossless Head Pruning through Automatic Peer Distillation for Language Models. - Bingbing Li; Zigeng Wang; Shaoyi Huang; Mikhail A. Bragin; Ji Li 006; Caiwen Ding (IJCAI, 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 (ISQED, 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)

Lagrangian Relaxation for Mixed-Integer Linear Programming: Importance, Challenges, Recent Advancements, and Opportunities. - Mikhail A. Bragin (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)

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)

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)

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., 2023)

Reforming Quantum Microgrid Formation. - Chaofan Lin; Peng Zhang 015; Mikhail A. Bragin; Yacov A. Shamash (CoRR, 2024)

Near-Optimal Scheduling for IC Packaging Operations Considering Processing-Time Variations and Factory Practices. - Tsung-Han Tsai 004; Bing Yan; Peter B. Luh; Haw Ching Yang; Mikhail A. Bragin; Fan-Tien Cheng (IEEE Robotics Autom. Lett., 2024)

Survey on Lagrangian relaxation for MILP: importance, challenges, historical review, recent advancements, and opportunities. - Mikhail A. Bragin (Ann. Oper. Res., 2024)

Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling. - Anbang Liu; Peter B. Luh; Kailai Sun; Mikhail A. Bragin; Bing Yan (IEEE Trans Autom. Sci. Eng., 2024)

Reduced-Order Decomposition and Coordination Approach for Markov-Based Stochastic Unit Commitment With Distributed Wind Farms. - Niranjan Raghunathan; Zongjie Wang; Mikhail A. Bragin; Peter B. Luh; Bing Yan; Tianqiao Zhao; Meng Yue (IEEE Access, 2025)

Accelerating Level-Value Adjustment for the Polyak Stepsize. - Anbang Liu; Mikhail A. Bragin; Xi Chen 036; Xiaohong Guan (J. Optim. Theory Appl., 2025)