Solving and benchmarking QUBO problems with Gurobi in…
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Solving and benchmarking QUBO problems with Gurobi in Python
State-of-the-art classical optimizer Gurobi for Quadratic Unconstrained Binary Optimization (QUBO) problems.
The core `gurobipy` implementation for QUBO is relatively compact:
```python
model = gp.Model()
x = model.addMVar(n, vtype=GRB.BINARY)
model.setObjective(x @ Q @ x, GRB.MINIMIZE)
model.optimize()
solution = x.X.astype(int)
objective = model.ObjVal
```
Complete workflow in Python.
First formulate a graph problem (weighted Max-Cut) as QUBO, solve it with Gurobi, benchmark increasingly large instances, and understand what the solver is doing beyond the `optimize()` call.
Interested in feedback on the modeling, benchmarking methodology, and which additional Gurobi metrics would make the comparison more rigorous.
#programming #technology #dev
State-of-the-art classical optimizer Gurobi for Quadratic Unconstrained Binary Optimization (QUBO) problems.
The core `gurobipy` implementation for QUBO is relatively compact:
```python
model = gp.Model()
x = model.addMVar(n, vtype=GRB.BINARY)
model.setObjective(x @ Q @ x, GRB.MINIMIZE)
model.optimize()
solution = x.X.astype(int)
objective = model.ObjVal
```
Complete workflow in Python.
First formulate a graph problem (weighted Max-Cut) as QUBO, solve it with Gurobi, benchmark increasingly large instances, and understand what the solver is doing beyond the `optimize()` call.
Interested in feedback on the modeling, benchmarking methodology, and which additional Gurobi metrics would make the comparison more rigorous.
#programming #technology #dev
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