# sampling

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

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.