Some really good performance improvements. Striking are the numbers for non-convex MIQCP models.
Performance Improvements
With Gurobi 9.1, the Gurobi Optimizer registered notable performance improvements across multiple problem types including:
- Primal simplex: 17% faster overall, 37% faster on models that take at least 100 seconds.
- Dual simplex: 29% faster overall, 66% faster on models that take at least 100 seconds.
- Barrier: 15% faster overall, 34% faster on models that take at least 100 seconds.
- Mixed-integer linear programming (MILP): 5% faster overall, 9% faster on models that take at least 100 seconds.
- Convex mixed-integer quadratic programming (MIQP): 5% faster overall, 20% faster on models that take at least 100 seconds.
- Convex mixed-integer quadratically constrained programming (MIQCP): 13% faster overall, 57% faster on models that take at least 100 seconds.
- Non-convex mixed-integer quadratically constrained programming (non-convex MIQCP): 4x faster overall, 9x faster on models that take at least 100 seconds.
- Irreducible Infeasible Subset (IIS) computation: 2.6x faster overall, 5.7x faster on models that take at least 100 seconds.
- Better MIP feasible solutions: Heuristics are significantly better at finding high-quality solutions earlier.
New Features
The new features in the release include:
- NoRel Heuristic: This new heuristic finds high-quality solutions in situations where the linear programming (LP) relaxation of the mixed-integer programming (MIP) problem is too expensive to solve.
- Integrality Focus: This new feature allows users to be much stricter on integrality constraints, thus avoiding many undesirable results (including trickle flows) that can come from small integrality violations.
- Python Matrix API Enhancements: Gurobi’s Python interface – gurobipy – has been extended and improved to better support matrix-oriented modeling.
- Pip Install Support: Users can now utilize pip, a Python tool, to install Gurobi in their Python environment.
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