I have a large MILP that I build with cvxpy and want to solve with GUROBI. When I give use the solve() function of cvxpy it take a really really really long time to setup and does not start solving for hours. Whilest doing that only 1 core of my cluster is being used. It is used for 100%. I would like to use multiple cores to build the model so that the process of building the model does not take so long. Running grbprobe also shows that gurobi knows about the other cores and for solving the problem it uses multiple cores.
I have tried to run with different flags i.e. turning presolve off and on or giving the number of Threads to be used (this seemed like i didn't even for the solving.
I also have reduce the number of constraints in the problem and it start solving much faster which means that this is definitively not a problem of the model itself.
The problem in it's normal state should have 2200 constraints i reduce it to 150 and it took a couple of seconds until it started to search for a solution.
The problem is that I don't see anything since it takes so long to get the ""set username parameters"" flag and I don't get any information on what the computer does in the mean time.
Is there a way to tell GUROBI or CVXPY that it can take more cpus for the build-up?
Is there another way to solve this problem?
Sorry. The first part of the solve (cvxpy model generation, setup, presolving, scaling, solving the root, preprocessing) is almost completely serial. The parallel part is when it really starts working on the branch-and-bound tree. For many problems, the parallel part is by far the most expensive, but not for all.
This is not only the case for Gurobi. Other high-end solvers have the same behavior.
There are options to do less presolving and preprocessing. That may get you earlier in the B&B. However, usually, it is better not to touch these options.
Running things with verbose=True may give you more information. If you have more detailed questions, you may want to share the log.
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I have to solve a linear program with a very large number of constraints. I use MOSEK (mosekopt, with MSK_IPAR_INTPNT_BASIS set equal to MSK_BI_NEVER to save time).
The solver takes time to solve the program due to the large dimension.
I thought about manually coding the following iterative procedure:
Take a random subset of constraints and solve the restricted linear program.
If a solution of the restricted linear program does not exist, stop.
If a solution of the restricted linear program exists, check if it is a solution of the original linear program. If yes, stop. If not, repeat from 1. with a larger set of constraints that includes the constraints of this iteration.
The procedure does not seem to produce a notable saving of time. I wonder whether this is because 1.,2.,3. are essentially what the solver does without needing my input. Could you advise?
Could I do improve things if, when moving from 3. to 1., I supply to mosekopt the old solution of the restricted linear program?
This may or may not be faster, than using Mosek on the complete problem. At least theoretical your approach is inferior.
You say nothing of the dimension of the problem that would be interesting to know.
Or how long it takes to solve the complete problem.
One issue tricky is how many and which constraints you are adding in 3. That will be very important.
I am using GAMS to solve a nonlinear programming problem. The problem size is about 500K rows, 500k columns, and 1M non-zeros. I found that it took long time to generate the model, sometimes even longer than solving the model. Are there any ways to speed up the generating process inside GAMS? Alternatively, if I switch to other platforms, are there any good choices to resolve this issue? Thanks.
As far as i know Gurobi resumes optimizing where it left after calling Model.Terminate() and then calling Model.Optimize() again. So I can terminate and get the best solution so far and then proceed.Now I want to do the same, but since I want to use parts of the suboptimal solution I need to set some variables to fixed values before I call Model.Optimize() again and optimize the rest of the model. How can i do this so that gurobi does not start all over again?
First, it sounds like you're describing a mixed-integer program (MIP); model modification is different for continuous optimization (linear programming, quadratic programming).
When you modify a MIP model, the tree information is no longer helpful. Instead, you must resolve the continuous (LP) relaxation and create a new branch-and-cut tree. However, the prior solution may still be used as a MIP start, which can reduce the solve time for the second model.
However, your method may be redundant with the RINS algorithm, which is an automatic feature of Gurobi MIP. You can control the behavior of RINS via the parameters RINS, SubMIPNodes and Heuristics.
Can anyone give me some tips to make a binary integer programming model faster?
I currently have a model that runs well with very small amount of variables but as soon as I increase the number of variables in my model SCIP keeps running without giving me an optimal solution. I'm currently using SCIP with Soplex to find an optimal solution.
You should have a look at the statistics (type display statistics in the interactive shell). Watch out for time consuming heuristics that don't find a solution and try disabling them. You should also play around with the parameters to find better suited settings for your instances (different branching rule or node selection). Without further information, though, we won't be able to help you.
I have a large MIP problem, and I use GLPSOL in GLPK to solve it. However, solving the LP relaxation problem takes many iterations, and each iteration the obj and infeas value are all the same. I think it has found the optimal solution, but it won't stop and has continued to run for many hours. Will this happen for every large-scale MIP/LP problem? How can I deal with such cases? Can anyone give me any suggestions about this? Thanks!
The problem of solving MIPs is NP-complete in general, which means that there are instances which can't be solved efficiently. But often our problems have enough structure, so that heuristics can help to solve these models. This allowed huge gains in solving-capabilities in the last decades (overview).
For understanding the basic-approach and understanding what exactly is the problem in your case (no progress in upper-bound, no progress in lower-bound, ...), read Practical Guidelines for Solving Difficult Mixed Integer Linear
Programs.
Keep in mind, that there are huge gaps between commercial solvers like Gurobi / Cplex and non-commercial ones in general (especially in MIP-solving). There is a huge amount of benchmarks here.
There are also a lot of parameters to tune. Gurobi for example has different parameter-templates: one targets fast findings of feasible solution; one targets to proof the bounds.
My personal opinion: compared to cbc (open-source) & scip (open-source but non-free for commercial usage), glpk is quite bad.