Tuesday, August 9, 2016 - 2:00pm - 3:30pm
Shabbir Ahmed (Georgia Institute of Technology)
Multistage stochastic programming (MSP) is a framework for sequential decision making under uncertainty where the decision space is typically high dimensional and involves complicated constraints, and the uncertainty is modeled by a general stochastic process. In the traditional risk neutral setting, the goal is to find a sequence of decisions or a policy so as to optimize an expected value objective. MSP has found applications in a variety of important sectors including energy, finance, manufacturing, services, and natural resources.
Monday, August 8, 2016 - 9:00am - 10:30am
Jeff Linderoth (University of Wisconsin, Madison)
This lecture gives an introduction to modeling optimization
problems where parameters of the problem are uncertain. The primary
focus will be on the case when the uncertain parameters are modeled as
random variables. We will introduce both two-stage, recourse-based
stochastic programming and chance-constrained approaches. Statistics
that measure the value of computing a solution to the stochastic
problem will be introduced. We will show how to create
an equivalent extensive form formulations of the instances, so that
Monday, March 14, 2016 - 3:00pm - 4:00pm
Matthias Heinkenschloss (Rice University)
Many science and engineering problems lead to optimization problems governed by partial differential equations (PDEs), and in many of these problems some of the problem data are not known exactly. I focus on a class of such optimization problems where the uncertain data are modeled by random variables or random fields, and where decision variables (controls/designs) are deterministic and have to be computed before the uncertainty is observed. It is important that the uncertainty in problem data is adequately incorporated into the formulation of the optimization problem.
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