LinExpr.add_var / LinExpr.add_expr / LinExpr.add_term are way too slow

Consider the following function. It generates and solves a model structurally somewhat similar to a practical problem I am solving. (I tried to simplify it as much as possible)

def experiment_mip(n):
    start = time.time()

    model = mip.Model(sense=mip.MAXIMIZE, solver_name=mip.CBC)
    obj = []
    for i in range(n):
        var = model.add_var(f"var_{i}", lb=0.5, ub=1.0)
        a = random.random()  - 0.5  # Just a dummy value for illustration
        obj.append(var * a)
        constraint = mip.xsum(obj) <= 1.0
        model.add_constr(constraint, f"constraint_{i}")
    model.objective = mip.xsum(obj)

    construction_time = time.time() - start
    start = time.time()

    model.optimize()

    solution_time = time.time() - start
    return {"Model construction time": construction_time, "Solution time": solution_time}

Now, try running this for example for n=4000. The solution is much faster than the construction of the model. My measurements suggest that the solution time is asymptotically O(n^2) complexity but the construction is more than O(n^3). The profiler shows that that most time is spent in the LinExpr.add_var, LinExpr.add_expr and LinExpr.add_term functions. I think this is not acceptable and it should never take more time to construct than to solve a nontrivial problem.

I am not sure what the solution could be.

[tuliotoffolo]

I’ve just included a test considering 5 variations of the code @jachymb and @christian2022 posted here. The test example is available in this link and here are the ‘construction’ runtimes I got with n=10000:

using_addvar: 9.426069974899292
using_inplace_op: 9.878501176834106
using_dict: 11.112802028656006
using_lists: 14.583881855010986
recreating_linexpr: 74.6143479347229

@rschwarz: you can now create a LinExpr from a dictionary or two lists (variables and coeffs).

[christian2022]

With these minor changes (see ###), on my laptop construction time is 1/3 of solution time for n = 4000

def experiment_mip(n):
    start = time.time()

    model = mip.Model(sense=mip.MAXIMIZE, solver_name=mip.CBC)
    obj = LinExpr() ### do not build array of LinExpr but directly append to LinExpr
    for i in range(n):
        var = model.add_var(f"var_{i}", lb=0.5, ub=1.0)
        a = random.random()  - 0.5  # Just a dummy value for illustration
        obj.add_var(var, a) ### do not create intermediate LinExpr
        constraint = obj <= 1.0 ### no need to mip.xsum as obj is already the sum
        model.add_constr(constraint, f"constraint_{i}")
    model.objective = obj

    construction_time = time.time() - start
    start = time.time()

    model.optimize()

    solution_time = time.time() - start
    return {"Model construction time": construction_time, "Solution time": solution_time}