Optimization under Uncertainty

2018-2019

Course Objective

Students who pass this course succesfully, (i) can devlelop learning
and optimization algorithms in a stochastic environment, (ii) are able
to mathemtically analyze the properties of learning and optimization
alogirthms, and (iii) can perfom an optimziaton study and report on
their findings.

Course Content

This is course on advanced simulation techniques. The methodological
part of the course focusses on the theory of recursive learning and
optimization algorithms known as stochastic approximation.

Topics coverd in this course are, among others, machine learning in a
stochastic environment, design of learning algorithms from ODEs, and
gradient estimation algorithms.

Teaching Methods

Combined lectures and tutorials

Method of Assessment

Final exam – Individual assessment
Individual assignment - Individual assessment

Entry Requirements

Analysis, basic probability theory, stochastic processes, basic
programming

Literature

Handout of monograph “Gradient based Stochastic Optimization”, F.
Vásquez-Abad and B. Heidergott, 2017.

Target Audience

The course is suitable to be taken in an exchange program

Additional Information

In presence of uncertainty, gradients typically fail to be available in
analytical form and optimization has to resort to simulation-based
algorithms. Unbiased gradient estimators are a main ingredient in
simulation-based optimization methods. The focus of this course is on
unbiased gradient estimators and their application in stochastic
simulation-based optimization and learning algorithms. Next to classical
stochastic gradient methods, this course also covers a range of related
topics such as model and parameter insecurity, robust optimization and
sample average approximation. Applications will stem from a wide range
of domains from Financial Engineering over Inventory Management to
Waiting Time Minimization.

General Information

Course Code E_EORM_OPTU
Credits 6 EC
Period P2
Course Level 400
Language of Tuition English
Faculty School of Business and Economics
Course Coordinator prof. dr. B.F. Heidergott
Examiner prof. dr. B.F. Heidergott
Teaching Staff

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Seminar, Lecture
Target audiences

This course is also available as: