# Optimizing over thin regions:

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### Optimizing over thin regions:

Now we move to our second thought experiment: Consider a feasible region consisting of a triangle in with a very wide base and very short height, as depicted here:

Consider the case where the ratio of the base to the height is on the order of , and that we consider a nominal objective function as in the figure.

In theory, the optimal solution should be the apex of the triangle, but assume that we randomly perturb both the right-hand side and the objective function with terms in the order of . What will happen with the numerical solution?

To perform the experiment, we execute the code thinOpt.py, where we perform a series of re-optimizations with different perturbations as described above. To be more precise, whenever the new computed solution is further from the mathetical solution by more than it has been in previous trials, we print:

• The new maximum distance among solutions.
• The current iteration.
• The (KappaExact atribute) value for the current optimal basis.
• The bound violation as reported by Gurobi for the current solution.
• The constraint violation as reported by Gurobi for the current solution.
• The dual violation as reported by Gurobi for the current solution.

Sample output is shown below:

New maxdiff 4e+16 Iter 0 Kappa 3.31072 Violations: 0 0 0
New maxdiff 4e+16 Iter 1 Kappa 3.31072 Violations: 0 0 0
New maxdiff 4e+16 Iter 2 Kappa 3.31072 Violations: 0 0 0
New maxdiff 4e+16 Iter 7 Kappa 3.31072 Violations: 0 0 0
New maxdiff 4e+16 Iter 83 Kappa 3.31072 Violations: 0 0 2.64698e-23
New maxdiff 4e+16 Iter 194 Kappa 3.31072 Violations: 0 0 0
New maxdiff 4e+16 Iter 1073 Kappa 3.31072 Violations: 0 1.13687e-13 0
New maxdiff 4e+16 Iter 4981 Kappa 3.31072 Violations: 0 0 0
New maxdiff 4e+16 Iter 19514 Kappa 3.31072 Violations: 0 0 0
New maxdiff 4e+16 Iter 47117 Kappa 3.31072 Violations: 0 0 0
New maxdiff 4e+16 Iter 429955 Kappa 3.31072 Violations: 0 0 0
New maxdiff 4e+16 Iter 852480 Kappa 3.31072 Violations: 0 0 0


Results look very different from what we saw in our first test. The distance between the solution to the unperturbed model and the solution to the perturbed one is huge, even from the very first iteration. Also, the values are relatively small, and the reported primal, dual, and bound violations are almost zero. So, what happened? Note that when we choose , we are choosing an optimal point where a small tilting of the objective function may move us to another extreme point very far away, and hence the large norm. This is possible because the region is very large and, in principle, without any bounds, i.e. this is related to the case of -optimal solutions and very long sides.

Again, we encourage you to play with this example. For example, what would happen if the nominal objective function is ?