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Course Catalog
Course Catalog
IE 602 Stochastic Programming
3 Credits
Stochastic programming is one of the fundamental
approaches that can be used to model
decision-making under uncertainty. It is concerned with the
mathematical programming problems, where the uncertain
problem parameters are represented by random variables,
and it extends deterministic optimization by explicitly
accounting for the uncertainty already in the modeling
age. This course will provide a broad overview
of the main themes and methods of the subject.
This course covers various optimization models
(chance-constrained optimization, two-stage
stochastic programming models, optimization with risk
measures, etc.), as well as their mathematical
programming-based solution methods and applications to
practical problems. Since stochastic programs are
computationally challenging, there is a particular
emphasis in this course on algorithmic tools (especially,
on decomposition-based algorithms) for
solving large-scale instances.
Last Offered Terms
Course Name
SU Credit
Spring 2016-2017
Stochastic Programming
3
Prerequisite: IE 501 - Doctorate - Min Grade D
or IE 501 - Masters - Min Grade D
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
| IE 602 Stochastic Programming | 3 Credits | ||||||
|---|---|---|---|---|---|---|---|
| Stochastic programming is one of the fundamental approaches that can be used to model decision-making under uncertainty. It is concerned with the mathematical programming problems, where the uncertain problem parameters are represented by random variables, and it extends deterministic optimization by explicitly accounting for the uncertainty already in the modeling age. This course will provide a broad overview of the main themes and methods of the subject. This course covers various optimization models (chance-constrained optimization, two-stage stochastic programming models, optimization with risk measures, etc.), as well as their mathematical programming-based solution methods and applications to practical problems. Since stochastic programs are computationally challenging, there is a particular emphasis in this course on algorithmic tools (especially, on decomposition-based algorithms) for solving large-scale instances. | |||||||
|
|||||||
| Prerequisite: IE 501 - Doctorate - Min Grade D | |||||||
| or IE 501 - Masters - Min Grade D | |||||||
| Corequisite: __ | |||||||
| ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) | |||||||
| General Requirements: | |||||||