Quicker data import for problems already defined in the standardized format

[Volkan Özcan]

Hello,

I have the optimization problem defined already in the standard format (there is an objective vector, A_ineq inequality and A_eq equality matrices with the corresponding b_ineq and b_eq vectors). 

I can import them as a pyomo model with the following code, but it takes too much time if matrices are big. I guess this is because of the loops; is there a way to make this import more quickly?

Best wishes

# Create a model

model = pyo.ConcreteModel()

# Number of variables

n_vars = len(f_obj)

# Define variables, all binary

model.x = pyo.Var(range(n_vars), domain=pyo.Binary)

# Objective function

model.objective = pyo.Objective(expr=sum(f_obj[i] * model.x[i] for i in range(n_vars)), sense=pyo.minimize)

# Adding inequality constraints

model.ineq_constraints = pyo.ConstraintList()

for i in range(len(Aineq)):

    model.ineq_constraints.add(sum(Aineq[i][j] * model.x[j] for j in range(n_vars)) <= bineq[i])

# Adding equality constraints

model.eq_constraints = pyo.ConstraintList()

for i in range(len(Aeq)):

    model.eq_constraints.add(sum(Aeq[i][j] * model.x[j] for j in range(n_vars)) == beq[i])