CODEWITHSUNDEEP
integercplexmixedgams-math
PM0087
PM0087

Reputation: 132

CPLEX Error 3019: Failure to solve MIP subproblem

I have written my Mixed-Ineteger model (MIP) in GAMS and trying to solve it with CPLEX. I encountered this error:

CPLEX Error 3019: Failure to solve MIP subproblem

I have 4 cores and 16 GB RAM on my PC. The optimality gap is set to 1%.

After a lot of searching, I found some suggestions (no solution), and I tried them all, yet no luck. Every time it takes about 30min-60min until the optimization terminates with an error. These are the combinations I tried:

  1. I increased the iteration limits to 1 million. Error 3019.
  2. Some relaxation "feasoptmode 3". Error 3019.
  3. Give priority to feasibility over optimality by using "mipemphasis 1". Error 3019.
  4. Change the algorithm for solving the subproblems by using "subalg 3". Error 3019.

Then adding relaxations:

  1. "feasopt 1", "feastoptmode 3", "mipemphasis 1". Out-of-memory

Adding heuristics as well:

  1. "feasopt 1", "feastoptmode 3", "mipemphasis 1", "fpheur 2" and "userheurfreq 20". Out-of-memory
  2. "feasopt 1", "feastoptmode 3", "mipemphasis 1", "fpheur 2" and "userheurfreq 30", "subalg 4". Out-of-memory

[Update] Also,

  1. "mipemphasis 1" and "subalg 4". Error 3019.

  2. "mipemphasis 4", "subalg 5" and "preind 0". Error 3019.

  3. "fpheur -1", "heurfreq -1", "rinsheur -1", "preind 0", "mipemphasis 1", "mipsearch 1". Error 3019. (all heuristics are off)

So far, I had 2.5 million variables. I changed the model (study period) and reduced the number of variables by half in two different ways, again, same error.

I'm running out of ideas now.

Here are some of the links I have used (since I don't have enough credits in the website, I can't post more than two links. I put the most important ones):

I. Link1 (IBM): CPLEX strategies for finding good feasible solutions quickly when solving MIPs

II. Link2 (IBM): CPLEX Performance Tuning for Mixed Integer Programs

Upvotes: 0

Views: 1439

Answers (1)

PM0087
PM0087

Reputation: 132

Finally found the solution:

"Big M"

The big Ms I'm using in my model (for linearization and some other constraint) were responsible. The big M was too large; I removed four of the nines and viola. It took 3 days to figure this out.

I also increased the iteration limit to 2 million and the optimality gap to 2%. (iteration limit wasn't necessary, but the optimality gap certainly helped). It returned a solution after 5 hours.

Upvotes: 1

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