使用SCIP解决混合整数二次程序

Question

I have a mixed integer quadratic program (MIQP) which I would like to solve using SCIP. The program is in the form such that on fixing the integer variables, the problem turns out to be a linear program. And on fixing the the continuous variables it becomes a Integer Program. A simple example :

max. \\Sigma_{i} n_i * f_i(x_i)

such that.
n_1 * x_1 + n2 * x_2 < t
n_3 * x_1 + n2 * x_2 < m
.
.
many random quadratic constraints in n_i's and x_i's
so on

Here f_i is a concave piecewise linear function.
x_i's are continuous variables ( they take real values )
n_i's are integer variables

I am able to solve the problem using SCIP. But on problems with a large number of variables SCIP takes a lot of time to find the solution. I have particularly noticed that it does not find many primal solutions. Thus the rate at which the upper bound reduces is very slow. However, I could get better results by doing set heuristics emphasis aggressive.
It would be great if anyone can guide me on the following questions :
1) Is there any particular algorithm/ Software package which solves problems that fit perfectly into the model as described above ?
2) Suggestions on how to improve the rate at which primal solutions are found.
3) What type of branching can I use to get better results ?
4) Any guidance on improving performance would be really helpful.

I am okay with relaxing the integer constraints as well.

Thanks

Answer 1

1) The algorithm in SCIP should fit your problem. There are other software packages that implement similar algorithms, eg, BARON and ANTIGONE.

2) Have a look which primal heuristics were successful in your run and change their parameters to run them more frequently.

3) No idea. Default should be ok.

4) Make sure that your variables have good bounds. Tighter bounds allow for a tighter relaxation to be constructed.

If you can post an instance of your problem somewhere, or a log of a SCIP run, including the detailed statistics at the end, maybe someone can give more hints on what to improve.