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from ortools.linear_solver import pywraplp
def main():
# Create the mip solver with the SCIP backend.
solver = pywraplp.Solver.CreateSolver('SCIP')
infinity = solver.infinity()
# x and y are integer non-negative variables.
x = solver.IntVar(3, infinity, 'x')
y = solver.IntVar(0, infinity, 'y')
t = solver.IntVar(0, infinity, 't')
print('Number of variables =', solver.NumVariables())
solver.Add(t == x-2)
# x + 7 * y <= 17.5.
solver.Add(y == 2*x/t)
# x <= 3.5.
# solver.Add(x <= 3.5)
print('Number of constraints =', solver.NumConstraints())
# Maximize x + 10 * y.
solver.Maximize(0)
status = solver.Solve()
if status == pywraplp.Solver.OPTIMAL:
print('Solution:')
print('Objective value =', solver.Objective().Value())
print('x =', x.solution_value())
print('y =', y.solution_value())
else:
print('The problem does not have an optimal solution.')
print('\nAdvanced usage:')
print('Problem solved in %f milliseconds' % solver.wall_time())
print('Problem solved in %d iterations' % solver.iterations())
print('Problem solved in %d branch-and-bound nodes' % solver.nodes())
if __name__ == '__main__':
main()
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