3. Tutorial

1. Import the conv_opt package:

   import conv_opt
   

2. Create a model and, optionally, name the model:

   model = conv_opt.Model(name='model')
   

   The default name is None.

3. Create variables; optionally set their names, types, and upper and lower bounds; and add them to models:

   var_1 = conv_opt.Variable(name='var_1', type=conv_opt.VariableType.continuous, lower_bound=0, upper_bound=1)
   model.variables.append(var_1)
   
   var_2 = conv_opt.Variable(name='var_2', type=conv_opt.VariableType.continuous, lower_bound=0, upper_bound=1)
   model.variables.append(var_2)
   

   The default name is None.

   The type argument must be one of the following values. The default type is continuous.

   • conv_opt.VariableType.binary

   • conv_opt.VariableType.integer

   • conv_opt.VariableType.continuous

   • conv_opt.VariableType.semi_integer

   • conv_opt.VariableType.semi_continuous

   • conv_opt.VariableType.partially_integer

   The default upper and lower bounds are None.

4. Set the objective expression and direction:

   model.objective_terms = [
       conv_opt.LinearTerm(var_1, 1.),
       conv_opt.QuadraticTerm(var_2, var_2, 1.),
   ]
   model.objective_direction = conv_opt.ObjectiveDirection.maximize
   

   objective_terms should be a list of linear (conv_opt.LinearTerm) and quadratic terms (conv_opt.QuadraticTerm). The arguments to the constructors of these class are the variables involved in the term and coefficient for the term.

   objective_direction can be either of following values. The default direction is minimize.

   • conv_opt.ObjectiveDirection.maximize

   • conv_opt.ObjectiveDirection.minimize

5. Create constraints; optionally set their names and upper and lower bounds; and add them to models:

   contraint_1 = conv_opt.Constraint([
       conv_opt.LinearTerm(var_1, 1),
       conv_opt.LinearTerm(var_2, -1),
   ], name='contraint_1', upper_bound=0, lower_bound=0)
   model.constraints.append(contraint_1)
   

   The first argument should be a list of linear terms.

   The default name and upper and lower bounds are None.

6. Configure the options for solving the model:

   options = conv_opt.SolveOptions(solver=conv_opt.Solver.cplex, presolve=Presolve.off, verbosity=False)
   

   The solver argument allows you to select a specific solver. The argument must be one of the following values. The default solver is GLPK.

   • conv_opt.Solver.cbc

   • conv_opt.Solver.cplex

   • conv_opt.Solver.cvxopt

   • conv_opt.Solver.glpk

   • conv_opt.Solver.gurobi

   • conv_opt.Solver.minos

   • conv_opt.Solver.mosek

   • conv_opt.Solver.quadprog

   • conv_opt.Solver.scipy

   • conv_opt.Solver.soplex

   • conv_opt.Solver.xpress

   The presolve aregument must be one of the following values. The default value is off.

   • conv_opt.Presolve.auto

   • conv_opt.Presolve.on

   • conv_opt.Presolve.off

   Please see the API documentation for information about additional options.

7. Solve the model:

   result = model.solve(options)
   if result.status_code != conv_opt.StatusCode.optimal:
       raise Exception(result.status_message)
   value = result.value
   primals = result.primals
   

   The result will be an instance of conv_opt.Result. The attributes of this class include:

   • status_code

   • status_message

   • value

   • primals

   • reduced_costs

   • duals

   status_code will be an instance of the conv_opt.StatusCode enumerated type.

8. Get diagnostic information about the model:

   stats = model.get_stats()
   

9. Convert the model to the lower level API of one of the solvers:

   options = conv_opt.SolveOptions(solver=conv_opt.Solver.cplex)
   cplex_model = model.convert(options)
   

10. Export the model to a file:

    filename='/path/to/file.ext'
    model.export(filename)
    

    conv_opt supports the following extensions:

    • alp

    • cbf

    • dpe: dual perturbed model

    • dua: dual

    • jtask: Jtask format

    • lp

    • mps

    • opf

    • ppe: perturbed model

    • rew: model with generic names in mps format

    • rlp: model with generic names in lp format

    • sav

    • task: Task format

    • xml: OSiL