FeasOpt accepts an infeasible model and selectively relaxes the bounds and constraints in a way that minimizes a weighted penalty function that you define. FeasOpt supports all types of infeasible models. In essence, FeasOpt is another optimization algorithm (analogous to phase I of the simplex algorithm). It tries to suggest the least change that would achieve feasibility. FeasOpt does not actually modify your model. Instead, it suggests a set of bounds and constraint ranges and produces the solution that would result from these relaxations. Your application can query this solution. It can also report these values directly, or it can apply these new values to your model, or you can run FeasOpt again with different weights perhaps to find a more acceptable relaxation.

The infeasibility on which FeasOpt works must be present explicitly in your model among its constraints and bounds. In particular, if you have set a MIP cutoff value with the idea that the cutoff value will render your model infeasible, and then you apply FeasOpt, you will not achieve the effect you expect. In such a case, you should add one or more explicit constraints to enforce the restriction you have in mind. In other words, add constraints rather than attempt to enforce a restriction through these parameters: