Feelings of Rejection

Written by: Paul Rubin

Primary Source: OR in an OB World

This is a quick (?) recap of an answer I posted on a support forum earlier today. I will couch it in terms somewhat specific to CPLEX, but with minor tweaks it should apply to other mixed-integer programming solvers as well.

It is possible to “warm start” CPLEX (and, I’m pretty sure, at least some other solvers) by feeding it an initial solution, or a partial solution (meaning values for some but not all variables). CPLEX will try to complete the solution if necessary, test for feasibility, and perhaps try to repair the solution if it is not feasible.

Today’s question involved a warm start that the author was confident was feasible, but that CPLEX was asserting was not (message: “Retaining values of one MIP start for possible repair”). I’ve seen essentially the same question other times, sometimes with the “retained for repair” message and sometimes with a flat-out rejection. What can you do if CPLEX disagrees that your starting solution is feasible?

First note that the more complete the starting solution, the easier it is for CPLEX to digest it. If you specify, say, values for only the integer variables, CPLEX will try to solve for the corresponding values of the continuous variables. That’s usually relatively painless, although (as with anything else MIP-related) your mileage will vary. If you specify values for just a portion of the integer variables, though, repairing the starting solution turns into a MIP problem unto itself. I’m pretty sure it is rarely the case that a warm start that incomplete will pay any dividends, even if CPLEX manages to repair it.

There is a parameter (MIP.Limits.RepairTries) that governs how much effort CPLEX spends trying to repair a starting solution. Higher values lead to more time spent trying to fix your warm start. The author of today’s question had that cranked up quite high, so he needed to look other places.

You may be convinced that the warm start you are supplying is feasible, but sometimes bugs happen, or a bit of rounding bites you in the sitzfleisch. A useful thing to try is fixing all the variables (or at least all the integer variables) to match the starting solution, by setting both the lower and upper bound of each variable to its value in the warm start. In effect, you collapse the feasible region down to a single point. Now tell CPLEX to solve the model. If it comes back immediately with your starting solution as the optimal solution, then your warm start really is feasible. On the other hand, if CPLEX claims the problem is now infeasible, run the conflict refiner and get a set of constraints that CPLEX feels are mutually inconsistent. Check your starting solution against those constraints manually and look for a discrepancy.

Update: It turns out that there is an API function (IloCplex.refineMIPStartConflict in the Java API, various pseudonyms in other APIs) that lets you skip the “fix the variables” step and directly identify a conflict in the starting solution (if, in fact, one exists).

One other possibility is numerical instability in the model (basically the MIP version of “gremlins”). Solve the model (without fixing any variables as described in the previous suggestion) and collect information on basis condition numbers. I’ve covered that subject before (“Ill-conditioned Bases and Numerical Instability“), so I won’t rehash it here. If you model is unstable, small rounding errors in your warm start can blow up into deal-breaking errors in constraint satisfaction. There is a parameter (Emphasis.Numerical) that tells CPLEX to spend extra effort on numerical precision, but the more general approach to instability is to look for reformulations of the model that are more stable. In particular, avoiding a mix of large and small constraint coefficients (such as is commonly induced by the dreaded “big M” method of formulating logical constraints) may be helpful.

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I'm an apostate mathematician, retired from a business school after 33 years of teaching mostly (but not exclusively) quantitative methods courses. My academic interests lie in operations research. I also study Tae Kwon Do a bit on the side.

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