for any and , then is strictly quasiconvex.
A quasiconcave function is a function whose negative is quasiconvex, and a strictly quasiconcave function is a function whose negative is strictly quasiconvex.
A (strictly) quasiconvex function has (strictly) convex lower contour sets, while a (strictly) quasiconcave function has (strictly) convex upper contour sets.
Optimization methods that work for quasiconvex functions come under the heading of quasiconvex programming. This comes under the broad heading of mathematical programming and generalizes both linear programming and convex programming.
There are also minimax theorems on quasiconvex functions, such as Sion's minimax theorem, which is a far-reaching generalization of the result of von Neumann and Oskar Morgenstern.
Examples
* Every convex function is quasiconvex.
* Any monotonic function is both quasiconvex and quasiconcave. More generally, a function which decreases up to a point and increases from that point on is quasiconvex.
*The floor function is an example of a quasiconvex function that is neither convex nor continuous.
ee also
* Convex function
* Pseudoconvex function
References
* Avriel, M., Diewert, W.E., Schaible, S. and Zang, I., "Generalized Concavity", Plenum Press, 1988.
External links
* [http://projecteuclid.org/euclid.pjm/1103040253 SION, M., "On general minimax theorems", Pacific J. Math. 8 (1958), 171-176.]
* [http://glossary.computing.society.informs.org/second.php Mathematical programming glossary]
* Charles Wilson, NYU Department of Economics, "Concave and Quasi-Concave Functions": http://www.wilsonc.econ.nyu.edu/UMath/Handouts/ums06h23convexsetsandfunctions.pdf