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Optimization Science

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Optimization Science

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Research Frontiers in Mixed-Integer Programming Algorithms

Development of branch-and-cut methods and advanced cutting plane techniques for solving combinatorial optimization problems.

Symmetry Breaking in Large-Scale Integer Programs
Quantum-Inspired Branching Strategies for Combinatorial Optimization
Neural Surrogate Models for MIP Solution Space Prediction
Hybrid Decomposition Methods at the Continuous-Discrete Boundary
Warm-Start Learning from Degenerate Integer Solutions
Adaptive Cutting Plane Hierarchies in High Dimensions
Submodular Structure Detection in Black-Box Integer Problems
Federated Learning for Distributed Integer Program Solving
Convexification Through Dormant Variable Activation Schemes
Lattice-Based Primal Heuristics for Nonconvex Integer Systems

All Optimization Science PhD categories