![]() We will consider two basic types of infeasibility. The FICO Xpress Optimizer provides functionality for diagnosing the cause of infeasibility in the user's problem.īefore we discuss the infeasibility diagnostics of the Optimizer we will define some types of infeasibility in terms of the type of problem it relates to and how the infeasibility is detected by the Optimizer. InfeasibilityĪ problem is said to be infeasible if no solution exists which satisfies all the constraints. In this chapter we discuss the various approaches and tools provided by the Optimizer for handling these issues. It is often difficult to deal with these issues since it is often difficult to diagnose the cause of the problems. Problem instability generally manifests in either long run times or spurious infeasibilities. This is typically because of large ratios between the largest and smallest coefficients in the rows or columns and the handling of the range of numerical values in the algorithm is causing floating point accuracy issues. Problem instability arises when the coefficient values of the problem are such that the optimization algorithms find it difficult to converge to a solution. ![]() When such a result is found it is typically not clear what it is about the formulation or the data that has caused the problem. Both situations often arise due to errors or shortcomings in the formulation or in the data defining the problem. An infeasible problem is a problem that has no solution while an unbounded problem is one where the constraints do not restrict the objective function and the objective goes to infinity. All users will, generally, encounter occasions in which an instance of the model they are developing is solved and found to be infeasible or unbounded. ![]()
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