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simple_lp_program_mb

<table align="left"> <td> <a href="https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/linear_solver/simple_lp_program_mb.ipynb">Run in Google Colab</a> </td> <td> <a href="https://github.com/google/or-tools/blob/main/ortools/linear_solver/samples/simple_lp_program_mb.py">View source on GitHub</a> </td> </table>

First, you must install ortools package in this colab.

python

%pip install ortools

Minimal example to call the GLOP solver using model_builder.

python

import math

from ortools.linear_solver.python import model_builder



def main():
    # Create the model.
    model = model_builder.Model()

    # Create the variables x and y.
    x = model.new_num_var(0.0, math.inf, "x")
    y = model.new_num_var(0.0, math.inf, "y")

    print("Number of variables =", model.num_variables)

    # x + 7 * y <= 17.5.
    ct = model.add(x + 7 * y <= 17.5)

    # x <= 3.5.
    model.add(x <= 3.5)

    print("Number of constraints =", model.num_constraints)

    # Maximize x + 10 * y.
    model.maximize(x + 10 * y)

    # Create the solver with the GLOP backend, and solve the model.
    solver = model_builder.Solver("glop")
    if not solver.solver_is_supported():
        return
    status = solver.solve(model)

    if status == model_builder.SolveStatus.OPTIMAL:
        print("Solution:")
        print("Objective value =", solver.objective_value)
        print("x =", solver.value(x))
        print("y =", solver.value(y))

        print("dual_value(ct) =", solver.dual_value(ct))
        print("reduced_cost(x) =", solver.reduced_cost(x))
    else:
        print("The problem does not have an optimal solution.")

    print("\nAdvanced usage:")
    print("Problem solved in %f seconds" % solver.wall_time)


main()