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Google OR-Tools v9.14
a fast and portable software suite for combinatorial optimization
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Classes | |
class | Model |
class | UpdateTracker |
A solver independent library for modeling optimization problems. Example use to model the optimization problem: max 2.0 * x + y s.t. x + y <= 1.5 x in {0.0, 1.0} y in [0.0, 2.5] model = mathopt.Model(name='my_model') x = model.add_binary_variable(name='x') y = model.add_variable(lb=0.0, ub=2.5, name='y') # We can directly use linear combinations of variables ... model.add_linear_constraint(x + y <= 1.5, name='c') # ... or build them incrementally. objective_expression = 0 objective_expression += 2 * x objective_expression += y model.maximize(objective_expression) # May raise a RuntimeError on invalid input or internal solver errors. result = mathopt.solve(model, mathopt.SolverType.GSCIP) if result.termination.reason not in (mathopt.TerminationReason.OPTIMAL, mathopt.TerminationReason.FEASIBLE): raise RuntimeError(f'model failed to solve: {result.termination}') print(f'Objective value: {result.objective_value()}') print(f'Value for variable x: {result.variable_values()[x]}')