polish lp code
This commit is contained in:
Laurent Perron
@@ -15,89 +15,90 @@
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# [START import]
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from __future__ import print_function
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from ortools.linear_solver import pywraplp
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# [END import]
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# [START data_model]
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def create_data_model():
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"""Stores the data for the problem."""
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data = {}
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# Locations in block units
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data['constraint_coeffs'] = [
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[5, 7, 9, 2, 1],
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[18, 4, -9, 10, 12],
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[4, 7, 3, 8, 5],
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[5, 13, 16, 3, -7],
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]
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data['bounds'] = [250, 285, 211, 315]
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data['obj_coeffs'] = [7, 8, 2, 9, 6]
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data['num_vars'] = 5
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data['num_constraints'] = 4
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return data
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"""Stores the data for the problem."""
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data = {}
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# Locations in block units
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data['constraint_coeffs'] = [
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[5, 7, 9, 2, 1],
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[18, 4, -9, 10, 12],
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[4, 7, 3, 8, 5],
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[5, 13, 16, 3, -7],
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]
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data['bounds'] = [250, 285, 211, 315]
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data['obj_coeffs'] = [7, 8, 2, 9, 6]
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data['num_vars'] = 5
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data['num_constraints'] = 4
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return data
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# [END data_model]
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def main():
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# [START data]
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data = create_data_model()
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# [END data]
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# [START data]
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data = create_data_model()
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# [END data]
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# [START solver]
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# MOE:begin_strip
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# Create the mip solver with the CBC backend.
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solver = pywraplp.Solver(
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'simple_mip_program',
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pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)
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# [END solver]
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# [START variables]
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infinity = solver.infinity()
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x = {}
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for j in range(data['num_vars']):
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x[j] = solver.IntVar(0, infinity, 'x[%i]' % j)
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print('Number of variables =', solver.NumVariables())
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# [END variables]
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# [START constraints]
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for i in range(data['num_constraints']):
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constraint = solver.RowConstraint(0, data['bounds'][i], '')
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# [START solver]
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# Create the mip solver with the CBC backend.
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solver = pywraplp.Solver('simple_mip_program',
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pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)
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# [END solver]
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# [START variables]
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infinity = solver.infinity()
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x = {}
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for j in range(data['num_vars']):
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constraint.SetCoefficient(x[j], data['constraint_coeffs'][i][j])
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print('Number of constraints =', solver.NumConstraints())
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# In Python, you can also set the constraints as follows.
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# for i in range(data['num_constraints']):
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# constraint_expr = \
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# [data['constraint_coeffs'][i][j] * x[j] for j in range(data['num_vars'])]
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# solver.Add(sum(constraint_expr) <= data['bounds'][i])
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# [END constraints]
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x[j] = solver.IntVar(0, infinity, 'x[%i]' % j)
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print('Number of variables =', solver.NumVariables())
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# [END variables]
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# [START objective]
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objective = solver.Objective()
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for j in range(data['num_vars']):
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objective.SetCoefficient(x[j], data['obj_coeffs'][j])
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objective.SetMaximization()
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# In Python, you can also set the objective as follows.
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# obj_expr = [data['obj_coeffs'][j] * x[j] for j in range(data['num_vars'])]
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# solver.Maximize(solver.Sum(obj_expr))
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# [END objective]
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# [START constraints]
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for i in range(data['num_constraints']):
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constraint = solver.RowConstraint(0, data['bounds'][i], '')
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for j in range(data['num_vars']):
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constraint.SetCoefficient(x[j], data['constraint_coeffs'][i][j])
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print('Number of constraints =', solver.NumConstraints())
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# In Python, you can also set the constraints as follows.
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# for i in range(data['num_constraints']):
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# constraint_expr = \
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# [data['constraint_coeffs'][i][j] * x[j] for j in range(data['num_vars'])]
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# solver.Add(sum(constraint_expr) <= data['bounds'][i])
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# [END constraints]
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# [START solve]
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status = solver.Solve()
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# [END solve]
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# [START print_solution]
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if status == pywraplp.Solver.OPTIMAL:
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print('Objective value =', solver.Objective().Value())
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# [START objective]
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objective = solver.Objective()
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for j in range(data['num_vars']):
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print(x[j].name(), ' = ', x[j].solution_value())
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print()
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print('Problem solved in %f milliseconds' % solver.wall_time())
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print('Problem solved in %d iterations' % solver.iterations())
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print('Problem solved in %d branch-and-bound nodes' % solver.nodes())
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else:
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print('The problem does not have an optimal solution.')
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# [END print_solution]
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objective.SetCoefficient(x[j], data['obj_coeffs'][j])
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objective.SetMaximization()
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# In Python, you can also set the objective as follows.
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# obj_expr = [data['obj_coeffs'][j] * x[j] for j in range(data['num_vars'])]
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# solver.Maximize(solver.Sum(obj_expr))
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# [END objective]
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# [START solve]
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status = solver.Solve()
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# [END solve]
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# [START print_solution]
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if status == pywraplp.Solver.OPTIMAL:
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print('Objective value =', solver.Objective().Value())
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for j in range(data['num_vars']):
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print(x[j].name(), ' = ', x[j].solution_value())
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print()
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print('Problem solved in %f milliseconds' % solver.wall_time())
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print('Problem solved in %d iterations' % solver.iterations())
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print('Problem solved in %d branch-and-bound nodes' % solver.nodes())
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else:
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print('The problem does not have an optimal solution.')
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# [END print_solution]
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if __name__ == '__main__':
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main()
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main()
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# [END program]
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