Optimization problems occasionally yield unbounded solutions. To find the cause one can modify the model and solve it to gain information. This is done through the imposition of “artificially” large bounds. Linear programming solvers discover unboundedness when they find a variable which is attractive to make larger, but find that the variable may be increased without limit.
Sometimes models do odd things like reporting problems as infeasible, stuck or falsely optimal when scaling is the real issue. To avoid this or correct such issues it is often desirable to check scaling and in turn rescale the model or ask the solvers to employ more aggressive scaling. In terms of solver scaling most LP/MIP solvers do automatic scaling and a number have the option to apply a more aggressive scaling to numerically difficult models, e.
I was talking to the GAMS staff and they informed me as to what their most common supportcalls involve. One of them involves fixing bad results. In this and the next couple of newsletters. I will cover how to diagnose problems within models that don’t work right. Also in the next section I will cover an integer programming issue that commonly comes up.