Summary: Could we minimize quasiconvex objectives in polynomial time?
Whenever an objective function of an optimization problem can be formed as a convex function, this is considered as victory. Typically efficient algorithms for finding global optimum exists.
However, I have not seen this done for quasiconvex functions. What is the major difference between them, that makes the second undesirable from the optimization point of view?