Mikhail Bragin

Assistant Research Professor (Engineering Profession)

Assistant Research Professor at the Electrical and Computer Engineering at the University of Connecticut. At the core of my research interests is the development of advanced mathematical mixed-integer optimization methods with analytically proven convergence and empirically verifiable fast performance with applications to problems in multiple disciplines such as smart power grids and smart manufacturing scheduling to address complex technical and societal challenges. Accordingly, my research is at the intersection of operations research, discrete optimization, systems theory, power engineering, computer science, and applied mathematics. Especially interesting are unconventional synergies of “classical” and “quantum” methods as well as machine learning approaches.


  • Data Interpolation by Near-Optimal Splines with Free Knots Using Linear Programming
  • Convergence of the Surrogate Lagrangian Relaxation Method
  • Solving payment cost co-optimization problems
  • An efficient surrogate optimization method for solving linear mixed-integer problems with cross-coupling constraints
  • An efficient approach for Unit Commitment and Economic Dispatch with combined cycle units and AC Power Flow
  • Novel exploitation of convex hull invariance for solving unit commitment by using surrogate Lagrangian relaxation and branch-and-cut
  • Surrogate Lagrangian relaxation and branch-and-cut for unit commitment with combined cycle units
  • A Novel Decomposition and Coordination Approach for Large Day-Ahead Unit Commitment with Combined Cycle Units
  • An efficient approach for solving mixed-integer programming problems under the monotonic condition
  • A Systematical Approach to Tighten Unit Commitment Formulations
  • A scalable solution methodology for mixed-integer linear programming problems arising in automation
  • Toward Coordinated Transmission and Distribution Operations
  • A decomposition and coordination approach for large-scale security constrained unit commitment problems with combined cycle units
  • Efficient surrogate optimization for payment cost co-optimization with transmission capacity constraints
  • Distributed and asynchronous unit commitment and economic dispatch
  • Grid integration of wind generation considering remote wind farms: Hybrid markovian and interval unit commitment
  • Economic Dispatch for a Distribution Network with Intermittent Renewables and Tap Changers
  • Energy-efficient building clusters
  • Exergy-efficient management of energy districts
  • Active fault management for microgrids
  • Novel formulation and resolution of job-shop scheduling problems
  • Effective modeling and resolution of generation-dependent ramp rates for unit commitment
  • An efficient surrogate subgradient method within Lagrangian relaxation for the Payment Cost Minimization problem
  • Payment cost minimization using Lagrangian relaxation and modified surrogate optimization approach
  • Efficient Operations of Micro-Grids with Meshed Topology and Under Uncertainty through Exact Satisfaction of AC-PF, Droop Control and Tap-Changer Constraints

Mikhail Bragin's public data