DATE: Thursday, October 26, 2006
LOCATION: E40-298
TIME: 4:15pm
Reception immediately following in the Philip M. Morse Reading Room, E40-106
ABSTRACT
We present ongoing experimental work involving Robust Optimization
in various applications, primarily (in this talk) Portfolio Optimization.
Robust Optimization is a framework for managing optimization in
the presence of data uncertainty, and is typically offered as a
counterpoint to Stochastic Programming, which requires known and
precise information on stochastic distributions of data. In principle,
the basic machinery of Robust Optimization can be extended to any
form of data uncertainty, no matter what its magnitude or structure
might be. A criticism that has been leveled against Robust Optimization is
that it can prove too conservative, or not conservative enough,
and, in particular, it has been criticized for assigning the same "weight" or
importance, to all possible realizations of data in the uncertainty
set. Part of this difficulty stems from the fact that the typical
uncertainty sets that have been examined in the literature are "well
behaved" whereas in several practical settings data can misbehave
in "malformed" or "lumpy" ways.
In this talk we will focus on the use of Benders' decomposition
(or, more generally, cutting plane algorithms) to handle realistic
and complex uncertainty models. Our implementations prove
successful on large, difficult portfolio optimization problems.
